{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conditional Autoencoder for Asset Pricing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook uses a dataset created using `yfinance` in the notebook [conditional_autoencoder_for_trading_data](05_conditional_autoencoder_for_trading_data.ipynb). The results will vary depending on which ticker downloads succeeded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:37:47.715567Z",
     "start_time": "2020-06-23T01:37:47.710170Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:37:49.874412Z",
     "start_time": "2020-06-23T01:37:47.875043Z"
    }
   },
   "outputs": [],
   "source": [
    "import sys, os\n",
    "from time import time\n",
    "from pathlib import Path\n",
    "from itertools import product\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Input, Dense, Dot, Reshape, BatchNormalization\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.callbacks import TensorBoard\n",
    "\n",
    "from sklearn.preprocessing import quantile_transform\n",
    "\n",
    "from scipy.stats import spearmanr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:37:49.981050Z",
     "start_time": "2020-06-23T01:37:49.875466Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using GPU\n"
     ]
    }
   ],
   "source": [
    "gpu_devices = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpu_devices:\n",
    "    print('Using GPU')\n",
    "    tf.config.experimental.set_memory_growth(gpu_devices[0], True)\n",
    "else:\n",
    "    print('Using CPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:37:49.990421Z",
     "start_time": "2020-06-23T01:37:49.982337Z"
    }
   },
   "outputs": [],
   "source": [
    "sys.path.insert(1, os.path.join(sys.path[0], '..'))\n",
    "from utils import MultipleTimeSeriesCV, format_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:37:49.997967Z",
     "start_time": "2020-06-23T01:37:49.991475Z"
    }
   },
   "outputs": [],
   "source": [
    "idx = pd.IndexSlice\n",
    "sns.set_style('whitegrid')\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:37:50.006494Z",
     "start_time": "2020-06-23T01:37:49.999295Z"
    }
   },
   "outputs": [],
   "source": [
    "results_path = Path('results', 'asset_pricing')\n",
    "if not results_path.exists():\n",
    "    results_path.mkdir(parents=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:37:50.015416Z",
     "start_time": "2020-06-23T01:37:50.007620Z"
    }
   },
   "outputs": [],
   "source": [
    "characteristics = ['beta', 'betasq', 'chmom', 'dolvol', 'idiovol', 'ill', 'indmom',\n",
    "                   'maxret', 'mom12m', 'mom1m', 'mom36m', 'mvel', 'retvol', 'turn', 'turn_std']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T23:44:31.406869Z",
     "start_time": "2020-06-22T23:44:31.286819Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.io.pytables.HDFStore'>\n",
      "File path: results/asset_pricing/autoencoder.h5\n",
      "/close                      frame        (shape->[7559,3657]) \n",
      "/factor/beta                frame        (shape->[2363186,1]) \n",
      "/factor/betasq              frame        (shape->[2363186,1]) \n",
      "/factor/chmom               frame        (shape->[2700206,1]) \n",
      "/factor/dolvol              frame        (shape->[2806487,1]) \n",
      "/factor/idiovol             frame        (shape->[2363186,1]) \n",
      "/factor/ill                 frame        (shape->[2572109,1]) \n",
      "/factor/indmom              frame        (shape->[2849516,1]) \n",
      "/factor/maxret              frame        (shape->[2856033,1]) \n",
      "/factor/mom12m              frame        (shape->[2700206,1]) \n",
      "/factor/mom1m               series       (shape->[2871156])   \n",
      "/factor/mom36m              frame        (shape->[2361477,1]) \n",
      "/factor/mvel                frame        (shape->[2874317,1]) \n",
      "/factor/retvol              frame        (shape->[2871156,1]) \n",
      "/factor/turn                frame        (shape->[2774987,1]) \n",
      "/factor/turn_std            frame        (shape->[2813541,1]) \n",
      "/metadata                   frame        (shape->[1,3])       \n",
      "/model_data                 frame        (shape->[5152713,17])\n",
      "/returns                    frame        (shape->[1565,3657]) \n",
      "/volume                     frame        (shape->[7559,3657]) \n"
     ]
    }
   ],
   "source": [
    "with pd.HDFStore(results_path / 'autoencoder.h5') as store:\n",
    "    print(store.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weekly returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T23:44:35.871021Z",
     "start_time": "2020-06-22T23:44:35.420812Z"
    }
   },
   "outputs": [],
   "source": [
    "data = (pd.read_hdf(results_path / 'autoencoder.h5', 'returns')\n",
    "        .stack(dropna=False)\n",
    "        .to_frame('returns')\n",
    "        .loc[idx['1993':, :], :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T23:46:07.284420Z",
     "start_time": "2020-06-22T23:44:35.872277Z"
    }
   },
   "outputs": [],
   "source": [
    "with pd.HDFStore(results_path / 'autoencoder.h5') as store:\n",
    "    keys = [k[1:] for k in store.keys() if k[1:].startswith('factor')]\n",
    "    for key in keys:\n",
    "        data[key.split('/')[-1]] = store[key].squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T23:46:07.608147Z",
     "start_time": "2020-06-22T23:46:07.285887Z"
    }
   },
   "outputs": [],
   "source": [
    "characteristics = data.drop('returns', axis=1).columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T23:46:12.738347Z",
     "start_time": "2020-06-22T23:46:07.609479Z"
    }
   },
   "outputs": [],
   "source": [
    "data['returns_fwd'] = data.returns.unstack('ticker').shift(-1).stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T23:46:13.179869Z",
     "start_time": "2020-06-22T23:46:12.739479Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 5152713 entries, (Timestamp('1993-01-01 00:00:00', freq='W-FRI'), 'A') to (Timestamp('2019-12-27 00:00:00', freq='W-FRI'), 'ZYME')\n",
      "Data columns (total 17 columns):\n",
      " #   Column       Non-Null Count    Dtype  \n",
      "---  ------       --------------    -----  \n",
      " 0   returns      2770456 non-null  float64\n",
      " 1   beta         2360139 non-null  float64\n",
      " 2   betasq       2360139 non-null  float64\n",
      " 3   chmom        2626339 non-null  float64\n",
      " 4   dolvol       2704165 non-null  float64\n",
      " 5   idiovol      2360139 non-null  float64\n",
      " 6   ill          2489829 non-null  float64\n",
      " 7   indmom       2770303 non-null  float64\n",
      " 8   maxret       2748720 non-null  float64\n",
      " 9   mom12m       2626339 non-null  float64\n",
      " 10  mom1m        2760621 non-null  float64\n",
      " 11  mom36m       2358433 non-null  float64\n",
      " 12  mvel         2761081 non-null  float64\n",
      " 13  retvol       2760621 non-null  float64\n",
      " 14  turn         2677256 non-null  float64\n",
      " 15  turn_std     2705590 non-null  float64\n",
      " 16  returns_fwd  2769651 non-null  float64\n",
      "dtypes: float64(17)\n",
      "memory usage: 688.0+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info(null_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T23:46:13.882709Z",
     "start_time": "2020-06-22T23:46:13.181167Z"
    }
   },
   "outputs": [],
   "source": [
    "nobs_by_date = data.groupby(level='date').count().max(1)\n",
    "nobs_by_characteristic = pd.melt(data[characteristics].groupby(level='date').count(),\n",
    "                                 value_name='# Observations',\n",
    "                                 var_name=['Characteristic'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T23:46:14.475964Z",
     "start_time": "2020-06-22T23:46:13.883665Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"white\"):\n",
    "    fig, axes = plt.subplots(ncols=2, figsize=(14, 4))\n",
    "    sns.distplot(nobs_by_date, kde=False, ax=axes[0])\n",
    "    axes[0].set_title('# of Stocks per Week')\n",
    "    axes[0].set_xlabel('# of Observations')\n",
    "    sns.boxplot(x='Characteristic',\n",
    "                y='# Observations',\n",
    "                data=nobs_by_characteristic,\n",
    "                ax=axes[1],\n",
    "                palette='Blues')\n",
    "    axes[1].set_xticklabels(axes[1].get_xticklabels(),\n",
    "                            rotation=25,\n",
    "                            ha='right')\n",
    "    axes[1].set_title('# of Observation per Stock Characteristic')\n",
    "    sns.despine()\n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rank-normalize characteristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T23:47:32.684714Z",
     "start_time": "2020-06-22T23:46:14.477753Z"
    }
   },
   "outputs": [],
   "source": [
    "data.loc[:, characteristics] = (data.loc[:, characteristics]\n",
    "                                .groupby(level='date')\n",
    "                                .apply(lambda x: pd.DataFrame(quantile_transform(x, \n",
    "                                                                                 copy=True, \n",
    "                                                                                 n_quantiles=x.shape[0]),\n",
    "                                                              columns=characteristics,\n",
    "                                                              index=x.index.get_level_values('ticker')))\n",
    "                               .mul(2).sub(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T23:47:32.820178Z",
     "start_time": "2020-06-22T23:47:32.686156Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 5152713 entries, (Timestamp('1993-01-01 00:00:00', freq='W-FRI'), 'A') to (Timestamp('2019-12-27 00:00:00', freq='W-FRI'), 'ZYME')\n",
      "Data columns (total 17 columns):\n",
      " #   Column       Non-Null Count    Dtype  \n",
      "---  ------       --------------    -----  \n",
      " 0   returns      2770456 non-null  float64\n",
      " 1   beta         2360139 non-null  float64\n",
      " 2   betasq       2360139 non-null  float64\n",
      " 3   chmom        2626339 non-null  float64\n",
      " 4   dolvol       2704165 non-null  float64\n",
      " 5   idiovol      2360139 non-null  float64\n",
      " 6   ill          2489829 non-null  float64\n",
      " 7   indmom       2770303 non-null  float64\n",
      " 8   maxret       2748720 non-null  float64\n",
      " 9   mom12m       2626339 non-null  float64\n",
      " 10  mom1m        2760621 non-null  float64\n",
      " 11  mom36m       2358433 non-null  float64\n",
      " 12  mvel         2761081 non-null  float64\n",
      " 13  retvol       2760621 non-null  float64\n",
      " 14  turn         2677256 non-null  float64\n",
      " 15  turn_std     2705590 non-null  float64\n",
      " 16  returns_fwd  2769651 non-null  float64\n",
      "dtypes: float64(17)\n",
      "memory usage: 848.0+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info(null_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T23:47:32.824723Z",
     "start_time": "2020-06-22T23:47:32.821374Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FrozenList(['date', 'ticker'])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index.names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T23:47:36.277990Z",
     "start_time": "2020-06-22T23:47:32.826001Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>returns</th>\n",
       "      <th>beta</th>\n",
       "      <th>betasq</th>\n",
       "      <th>chmom</th>\n",
       "      <th>dolvol</th>\n",
       "      <th>idiovol</th>\n",
       "      <th>ill</th>\n",
       "      <th>indmom</th>\n",
       "      <th>maxret</th>\n",
       "      <th>mom12m</th>\n",
       "      <th>mom1m</th>\n",
       "      <th>mom36m</th>\n",
       "      <th>mvel</th>\n",
       "      <th>retvol</th>\n",
       "      <th>turn</th>\n",
       "      <th>turn_std</th>\n",
       "      <th>returns_fwd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.770456e+06</td>\n",
       "      <td>2.360139e+06</td>\n",
       "      <td>2.360139e+06</td>\n",
       "      <td>2.626339e+06</td>\n",
       "      <td>2.704165e+06</td>\n",
       "      <td>2.360139e+06</td>\n",
       "      <td>2.489829e+06</td>\n",
       "      <td>2.770303e+06</td>\n",
       "      <td>2.748720e+06</td>\n",
       "      <td>2.626339e+06</td>\n",
       "      <td>2.760621e+06</td>\n",
       "      <td>2.358433e+06</td>\n",
       "      <td>2.761081e+06</td>\n",
       "      <td>2.760621e+06</td>\n",
       "      <td>2.677256e+06</td>\n",
       "      <td>2.705590e+06</td>\n",
       "      <td>2.769651e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.056325e-03</td>\n",
       "      <td>-6.346325e-09</td>\n",
       "      <td>-5.837470e-07</td>\n",
       "      <td>-3.568177e-09</td>\n",
       "      <td>-1.705488e-06</td>\n",
       "      <td>-2.294799e-07</td>\n",
       "      <td>-5.689239e-07</td>\n",
       "      <td>-6.601156e-02</td>\n",
       "      <td>3.892305e-09</td>\n",
       "      <td>-2.118056e-07</td>\n",
       "      <td>-3.516596e-08</td>\n",
       "      <td>-2.538978e-07</td>\n",
       "      <td>7.696316e-08</td>\n",
       "      <td>-2.838747e-05</td>\n",
       "      <td>-7.312079e-07</td>\n",
       "      <td>-1.196178e-06</td>\n",
       "      <td>3.053796e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.971123e-02</td>\n",
       "      <td>5.776945e-01</td>\n",
       "      <td>5.776952e-01</td>\n",
       "      <td>5.776593e-01</td>\n",
       "      <td>5.776536e-01</td>\n",
       "      <td>5.776947e-01</td>\n",
       "      <td>5.776766e-01</td>\n",
       "      <td>5.943010e-01</td>\n",
       "      <td>5.776439e-01</td>\n",
       "      <td>5.776594e-01</td>\n",
       "      <td>5.776404e-01</td>\n",
       "      <td>5.776946e-01</td>\n",
       "      <td>5.776445e-01</td>\n",
       "      <td>5.776925e-01</td>\n",
       "      <td>5.776546e-01</td>\n",
       "      <td>5.776515e-01</td>\n",
       "      <td>5.971491e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-9.990779e-01</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-9.990779e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-2.083260e-02</td>\n",
       "      <td>-5.003208e-01</td>\n",
       "      <td>-5.003129e-01</td>\n",
       "      <td>-5.002485e-01</td>\n",
       "      <td>-5.002425e-01</td>\n",
       "      <td>-5.003047e-01</td>\n",
       "      <td>-5.002694e-01</td>\n",
       "      <td>-6.050328e-01</td>\n",
       "      <td>-5.002516e-01</td>\n",
       "      <td>-5.002384e-01</td>\n",
       "      <td>-5.001822e-01</td>\n",
       "      <td>-5.002966e-01</td>\n",
       "      <td>-5.002188e-01</td>\n",
       "      <td>-5.002597e-01</td>\n",
       "      <td>-5.001962e-01</td>\n",
       "      <td>-5.002272e-01</td>\n",
       "      <td>-2.083303e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.052650e-03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-7.303063e-02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.052504e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.457069e-02</td>\n",
       "      <td>5.003180e-01</td>\n",
       "      <td>5.003072e-01</td>\n",
       "      <td>5.002701e-01</td>\n",
       "      <td>5.002333e-01</td>\n",
       "      <td>5.003176e-01</td>\n",
       "      <td>5.002665e-01</td>\n",
       "      <td>4.275164e-01</td>\n",
       "      <td>5.002268e-01</td>\n",
       "      <td>5.002394e-01</td>\n",
       "      <td>5.002492e-01</td>\n",
       "      <td>5.002883e-01</td>\n",
       "      <td>5.002044e-01</td>\n",
       "      <td>5.002251e-01</td>\n",
       "      <td>5.002270e-01</td>\n",
       "      <td>5.002306e-01</td>\n",
       "      <td>2.456899e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            returns          beta        betasq         chmom        dolvol  \\\n",
       "count  2.770456e+06  2.360139e+06  2.360139e+06  2.626339e+06  2.704165e+06   \n",
       "mean   3.056325e-03 -6.346325e-09 -5.837470e-07 -3.568177e-09 -1.705488e-06   \n",
       "std    5.971123e-02  5.776945e-01  5.776952e-01  5.776593e-01  5.776536e-01   \n",
       "min   -9.990779e-01 -1.000000e+00 -1.000000e+00 -1.000000e+00 -1.000000e+00   \n",
       "25%   -2.083260e-02 -5.003208e-01 -5.003129e-01 -5.002485e-01 -5.002425e-01   \n",
       "50%    1.052650e-03  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "75%    2.457069e-02  5.003180e-01  5.003072e-01  5.002701e-01  5.002333e-01   \n",
       "max    4.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "\n",
       "            idiovol           ill        indmom        maxret        mom12m  \\\n",
       "count  2.360139e+06  2.489829e+06  2.770303e+06  2.748720e+06  2.626339e+06   \n",
       "mean  -2.294799e-07 -5.689239e-07 -6.601156e-02  3.892305e-09 -2.118056e-07   \n",
       "std    5.776947e-01  5.776766e-01  5.943010e-01  5.776439e-01  5.776594e-01   \n",
       "min   -1.000000e+00 -1.000000e+00 -1.000000e+00 -1.000000e+00 -1.000000e+00   \n",
       "25%   -5.003047e-01 -5.002694e-01 -6.050328e-01 -5.002516e-01 -5.002384e-01   \n",
       "50%    0.000000e+00  0.000000e+00 -7.303063e-02  0.000000e+00  0.000000e+00   \n",
       "75%    5.003176e-01  5.002665e-01  4.275164e-01  5.002268e-01  5.002394e-01   \n",
       "max    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "\n",
       "              mom1m        mom36m          mvel        retvol          turn  \\\n",
       "count  2.760621e+06  2.358433e+06  2.761081e+06  2.760621e+06  2.677256e+06   \n",
       "mean  -3.516596e-08 -2.538978e-07  7.696316e-08 -2.838747e-05 -7.312079e-07   \n",
       "std    5.776404e-01  5.776946e-01  5.776445e-01  5.776925e-01  5.776546e-01   \n",
       "min   -1.000000e+00 -1.000000e+00 -1.000000e+00 -1.000000e+00 -1.000000e+00   \n",
       "25%   -5.001822e-01 -5.002966e-01 -5.002188e-01 -5.002597e-01 -5.001962e-01   \n",
       "50%    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "75%    5.002492e-01  5.002883e-01  5.002044e-01  5.002251e-01  5.002270e-01   \n",
       "max    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "\n",
       "           turn_std   returns_fwd  \n",
       "count  2.705590e+06  2.769651e+06  \n",
       "mean  -1.196178e-06  3.053796e-03  \n",
       "std    5.776515e-01  5.971491e-02  \n",
       "min   -1.000000e+00 -9.990779e-01  \n",
       "25%   -5.002272e-01 -2.083303e-02  \n",
       "50%    0.000000e+00  1.052504e-03  \n",
       "75%    5.002306e-01  2.456899e-02  \n",
       "max    1.000000e+00  4.000000e+00  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T23:47:37.337515Z",
     "start_time": "2020-06-22T23:47:36.280131Z"
    }
   },
   "outputs": [],
   "source": [
    "data = data.loc[idx[:'2019', :], :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T23:47:38.071874Z",
     "start_time": "2020-06-22T23:47:37.338472Z"
    }
   },
   "outputs": [],
   "source": [
    "data.loc[:, ['returns', 'returns_fwd']] = data.loc[:, ['returns', 'returns_fwd']].clip(lower=-1, upper=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T23:47:38.400183Z",
     "start_time": "2020-06-22T23:47:38.072947Z"
    }
   },
   "outputs": [],
   "source": [
    "data = data.fillna(-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T23:47:38.860411Z",
     "start_time": "2020-06-22T23:47:38.401173Z"
    }
   },
   "outputs": [],
   "source": [
    "data.to_hdf(results_path / 'autoencoder.h5', 'model_data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:38:04.918850Z",
     "start_time": "2020-06-23T01:38:03.742683Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_hdf(results_path / 'autoencoder.h5', 'model_data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Key parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:38:04.983043Z",
     "start_time": "2020-06-23T01:38:04.947056Z"
    }
   },
   "outputs": [],
   "source": [
    "n_factors = 3\n",
    "n_characteristics = len(characteristics)\n",
    "n_tickers = len(data.index.unique('ticker'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:38:05.132090Z",
     "start_time": "2020-06-23T01:38:05.126184Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3657"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_tickers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:38:05.263982Z",
     "start_time": "2020-06-23T01:38:05.261601Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_characteristics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:38:08.653713Z",
     "start_time": "2020-06-23T01:38:08.575468Z"
    }
   },
   "outputs": [],
   "source": [
    "input_beta = Input((n_tickers, n_characteristics), name='input_beta')\n",
    "input_factor = Input((n_tickers,), name='input_factor')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stock Characteristics Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:38:09.914679Z",
     "start_time": "2020-06-23T01:38:08.996551Z"
    }
   },
   "outputs": [],
   "source": [
    "hidden_layer = Dense(units=8, activation='relu', name='hidden_layer')(input_beta)\n",
    "batch_norm = BatchNormalization(name='batch_norm')(hidden_layer)\n",
    "output_beta = Dense(units=n_factors, name='output_beta')(batch_norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Factor Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:38:09.922233Z",
     "start_time": "2020-06-23T01:38:09.915692Z"
    }
   },
   "outputs": [],
   "source": [
    "output_factor = Dense(units=n_factors, name='output_factor')(input_factor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:39:07.556869Z",
     "start_time": "2020-06-23T01:39:07.538448Z"
    }
   },
   "outputs": [],
   "source": [
    "output = Dot(axes=(2,1), name='output_layer')([output_beta, output_factor])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compile Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:39:08.072223Z",
     "start_time": "2020-06-23T01:39:08.052387Z"
    }
   },
   "outputs": [],
   "source": [
    "model = Model(inputs=[input_beta, input_factor], outputs=output)\n",
    "model.compile(loss='mse', optimizer='adam')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Automate model generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:39:08.428569Z",
     "start_time": "2020-06-23T01:39:08.419206Z"
    }
   },
   "outputs": [],
   "source": [
    "def make_model(hidden_units=8, n_factors=3):\n",
    "    input_beta = Input((n_tickers, n_characteristics), name='input_beta')\n",
    "    input_factor = Input((n_tickers,), name='input_factor')\n",
    "\n",
    "    hidden_layer = Dense(units=hidden_units, activation='relu', name='hidden_layer')(input_beta)\n",
    "    batch_norm = BatchNormalization(name='batch_norm')(hidden_layer)\n",
    "    \n",
    "    output_beta = Dense(units=n_factors, name='output_beta')(batch_norm)\n",
    "\n",
    "    output_factor = Dense(units=n_factors, name='output_factor')(input_factor)\n",
    "\n",
    "    output = Dot(axes=(2,1), name='output_layer')([output_beta, output_factor])\n",
    "\n",
    "    model = Model(inputs=[input_beta, input_factor], outputs=output)\n",
    "    model.compile(loss='mse', optimizer='adam')\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:39:09.106821Z",
     "start_time": "2020-06-23T01:39:09.089925Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_beta (InputLayer)         [(None, 3657, 15)]   0                                            \n",
      "__________________________________________________________________________________________________\n",
      "hidden_layer (Dense)            (None, 3657, 8)      128         input_beta[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_norm (BatchNormalization) (None, 3657, 8)      32          hidden_layer[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "input_factor (InputLayer)       [(None, 3657)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "output_beta (Dense)             (None, 3657, 3)      27          batch_norm[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "output_factor (Dense)           (None, 3)            10974       input_factor[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "output_layer (Dot)              (None, 3657)         0           output_beta[0][0]                \n",
      "                                                                 output_factor[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 11,161\n",
      "Trainable params: 11,145\n",
      "Non-trainable params: 16\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:39:11.106369Z",
     "start_time": "2020-06-23T01:39:11.104631Z"
    }
   },
   "outputs": [],
   "source": [
    "YEAR = 52"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:39:11.320502Z",
     "start_time": "2020-06-23T01:39:11.316765Z"
    }
   },
   "outputs": [],
   "source": [
    "cv = MultipleTimeSeriesCV(n_splits=5, \n",
    "                          train_period_length=20*YEAR,\n",
    "                          test_period_length=1*YEAR,\n",
    "                          lookahead=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:39:11.580448Z",
     "start_time": "2020-06-23T01:39:11.571160Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_train_valid_data(data, train_idx, val_idx):\n",
    "    train, val = data.iloc[train_idx], data.iloc[val_idx]\n",
    "    X1_train = train.loc[:, characteristics].values.reshape(-1, n_tickers, n_characteristics)\n",
    "    X1_val = val.loc[:, characteristics].values.reshape(-1, n_tickers, n_characteristics)\n",
    "    X2_train = train.loc[:, 'returns'].unstack('ticker')\n",
    "    X2_val = val.loc[:, 'returns'].unstack('ticker')\n",
    "    y_train = train.returns_fwd.unstack('ticker')\n",
    "    y_val = val.returns_fwd.unstack('ticker')\n",
    "    return X1_train, X2_train, y_train, X1_val, X2_val, y_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T18:07:27.591615Z",
     "start_time": "2020-06-22T18:07:27.589669Z"
    }
   },
   "outputs": [],
   "source": [
    "factor_opts = [2, 3, 4, 5, 6]\n",
    "unit_opts = [8, 16, 32]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T18:07:27.789602Z",
     "start_time": "2020-06-22T18:07:27.787718Z"
    }
   },
   "outputs": [],
   "source": [
    "param_grid = list(product(unit_opts, factor_opts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T18:07:28.159468Z",
     "start_time": "2020-06-22T18:07:28.157799Z"
    }
   },
   "outputs": [],
   "source": [
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T18:07:28.525597Z",
     "start_time": "2020-06-22T18:07:28.523718Z"
    }
   },
   "outputs": [],
   "source": [
    "cols = ['units', 'n_factors', 'fold', 'epoch', 'ic_mean', \n",
    "        'ic_daily_mean', 'ic_daily_std', 'ic_daily_median']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T21:31:17.057835Z",
     "start_time": "2020-06-22T18:07:39.107230Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:00:03 | 2 08 | 00-000 | -0.24% |  0.06% | -1.32%\n",
      "00:00:30 | 2 08 | 00-050 |  1.52% | -0.45% | -0.83%\n",
      "00:00:57 | 2 08 | 00-100 |  3.01% |  0.26% | -1.73%\n",
      "00:01:24 | 2 08 | 00-150 |  2.59% |  1.10% |  0.36%\n",
      "00:01:51 | 2 08 | 00-200 |  2.29% |  0.97% |  0.72%\n",
      "00:02:17 | 2 08 | 01-000 |  2.56% |  1.92% |  2.28%\n",
      "00:02:43 | 2 08 | 01-050 |  2.03% |  0.28% |  1.44%\n",
      "00:03:09 | 2 08 | 01-100 |  0.83% |  2.05% |  4.18%\n",
      "00:03:35 | 2 08 | 01-150 |  1.69% |  1.38% |  3.73%\n",
      "00:04:01 | 2 08 | 01-200 | -1.45% | -0.48% | -0.74%\n",
      "00:04:25 | 2 08 | 02-000 | -1.37% | -1.83% | -2.87%\n",
      "00:04:52 | 2 08 | 02-050 |  0.16% | -0.13% | -0.52%\n",
      "00:05:19 | 2 08 | 02-100 |  1.33% |  1.39% |  1.78%\n",
      "00:05:45 | 2 08 | 02-150 |  0.99% |  0.97% |  1.26%\n",
      "00:06:11 | 2 08 | 02-200 |  1.80% |  1.55% |  2.80%\n",
      "00:06:36 | 2 08 | 03-000 | -1.47% | -0.80% | -1.90%\n",
      "00:07:03 | 2 08 | 03-050 | -0.41% | -0.35% | -2.67%\n",
      "00:07:28 | 2 08 | 03-100 |  0.61% |  1.38% | -0.42%\n",
      "00:07:51 | 2 08 | 03-150 | -1.48% | -1.25% | -0.11%\n",
      "00:08:14 | 2 08 | 03-200 |  2.36% | -0.61% | -1.78%\n",
      "00:08:39 | 2 08 | 04-000 |  2.18% |  3.10% |  3.13%\n",
      "00:09:03 | 2 08 | 04-050 |  2.18% |  3.43% |  2.74%\n",
      "00:09:27 | 2 08 | 04-100 |  0.43% |  3.47% |  4.55%\n",
      "00:09:52 | 2 08 | 04-150 |  7.24% |  1.40% |  0.40%\n",
      "00:10:15 | 2 08 | 04-200 | -0.25% | -2.53% | -1.81%\n",
      "00:10:45 | 3 08 | 00-000 |  1.16% |  2.13% |  2.02%\n",
      "00:11:10 | 3 08 | 00-050 | -1.64% | -1.12% | -2.54%\n",
      "00:11:35 | 3 08 | 00-100 | -3.31% | -1.58% | -2.39%\n",
      "00:11:59 | 3 08 | 00-150 | -1.83% |  0.35% |  0.24%\n",
      "00:12:24 | 3 08 | 00-200 | -0.95% |  0.04% |  0.98%\n",
      "00:12:52 | 3 08 | 01-000 |  0.30% | -0.54% | -2.66%\n",
      "00:13:19 | 3 08 | 01-050 |  0.27% |  0.02% |  0.02%\n",
      "00:13:44 | 3 08 | 01-100 | -1.21% | -0.86% | -2.40%\n",
      "00:14:09 | 3 08 | 01-150 | -1.48% | -1.94% | -3.66%\n",
      "00:14:35 | 3 08 | 01-200 |  1.26% |  0.57% |  1.19%\n",
      "00:15:05 | 3 08 | 02-000 |  1.20% |  1.41% |  1.76%\n",
      "00:15:32 | 3 08 | 02-050 |  0.27% |  0.13% | -0.75%\n",
      "00:15:55 | 3 08 | 02-100 |  0.23% | -0.48% | -0.25%\n",
      "00:16:19 | 3 08 | 02-150 | -0.67% | -0.63% | -1.31%\n",
      "00:16:44 | 3 08 | 02-200 |  0.56% |  0.28% |  1.93%\n",
      "00:17:10 | 3 08 | 03-000 |  2.74% |  0.70% |  1.22%\n",
      "00:17:33 | 3 08 | 03-050 | -0.82% | -0.67% |  0.04%\n",
      "00:17:57 | 3 08 | 03-100 | -1.07% | -0.49% | -0.26%\n",
      "00:18:22 | 3 08 | 03-150 |  1.09% |  0.61% |  1.82%\n",
      "00:18:45 | 3 08 | 03-200 |  1.09% |  1.79% |  3.02%\n",
      "00:19:13 | 3 08 | 04-000 | -0.84% |  1.61% |  1.07%\n",
      "00:19:36 | 3 08 | 04-050 |  6.95% |  3.16% |  3.45%\n",
      "00:20:01 | 3 08 | 04-100 |  3.79% |  1.38% | -2.26%\n",
      "00:20:25 | 3 08 | 04-150 |  4.74% |  3.10% |  0.99%\n",
      "00:20:48 | 3 08 | 04-200 | -1.91% |  1.45% |  0.53%\n",
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      "00:22:35 | 4 08 | 00-150 |  1.26% |  0.35% |  0.04%\n",
      "00:23:02 | 4 08 | 00-200 | -0.73% | -0.74% | -1.75%\n",
      "00:23:29 | 4 08 | 01-000 |  4.26% |  2.73% |  3.18%\n",
      "00:23:58 | 4 08 | 01-050 | -0.02% | -1.51% | -4.52%\n",
      "00:24:29 | 4 08 | 01-100 |  0.23% | -1.32% | -3.70%\n",
      "00:24:56 | 4 08 | 01-150 | -3.61% | -0.85% | -1.70%\n",
      "00:25:25 | 4 08 | 01-200 | -0.08% | -0.11% | -0.08%\n",
      "00:25:53 | 4 08 | 02-000 |  2.77% |  1.86% |  1.71%\n",
      "00:26:22 | 4 08 | 02-050 |  0.74% |  0.64% | -0.91%\n",
      "00:26:48 | 4 08 | 02-100 |  0.41% | -0.28% | -1.71%\n",
      "00:27:14 | 4 08 | 02-150 |  0.42% | -0.40% |  0.35%\n",
      "00:27:42 | 4 08 | 02-200 |  0.43% | -0.41% | -0.03%\n",
      "00:28:13 | 4 08 | 03-000 |  3.59% |  0.13% |  0.30%\n",
      "00:28:43 | 4 08 | 03-050 |  2.14% |  0.80% |  1.99%\n",
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      "00:29:39 | 4 08 | 03-150 | -0.13% |  1.12% |  0.45%\n",
      "00:30:08 | 4 08 | 03-200 | -1.04% |  1.22% |  1.98%\n",
      "00:30:37 | 4 08 | 04-000 | -1.79% |  1.33% |  1.27%\n",
      "00:31:06 | 4 08 | 04-050 |  1.40% |  0.43% | -1.76%\n",
      "00:31:33 | 4 08 | 04-100 |  2.44% | -0.96% |  0.73%\n",
      "00:32:01 | 4 08 | 04-150 |  5.17% |  0.63% |  2.21%\n",
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      "00:33:02 | 5 08 | 00-000 |  3.80% |  2.82% |  1.80%\n",
      "00:33:29 | 5 08 | 00-050 |  3.87% |  3.01% |  3.17%\n",
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      "00:34:25 | 5 08 | 00-150 |  2.22% |  1.87% |  1.67%\n",
      "00:34:53 | 5 08 | 00-200 |  1.13% |  1.80% |  1.76%\n",
      "00:35:22 | 5 08 | 01-000 |  1.89% |  2.56% |  3.32%\n",
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      "00:38:22 | 5 08 | 02-050 |  1.49% |  1.15% |  0.97%\n",
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      "00:40:22 | 5 08 | 03-000 |  3.07% |  2.37% |  2.96%\n",
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      "00:51:50 | 6 08 | 03-000 |  6.52% |  5.19% |  4.39%\n",
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      "00:56:33 | 2 16 | 00-000 |  4.74% |  2.12% |  1.66%\n",
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      "01:05:30 | 2 16 | 03-150 |  0.06% |  0.77% |  0.74%\n",
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      "01:06:24 | 2 16 | 04-000 |  1.33% |  0.83% |  1.43%\n",
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      "01:08:47 | 3 16 | 00-000 |  3.41% |  2.46% |  2.47%\n",
      "01:09:17 | 3 16 | 00-050 | -3.07% | -0.67% | -1.29%\n",
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      "01:10:15 | 3 16 | 00-150 | -0.48% |  0.08% | -0.20%\n",
      "01:10:47 | 3 16 | 00-200 | -0.45% | -0.17% | -0.15%\n",
      "01:11:17 | 3 16 | 01-000 |  0.22% |  1.14% |  0.11%\n",
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      "01:12:13 | 3 16 | 01-100 |  0.36% |  1.31% |  2.50%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01:12:41 | 3 16 | 01-150 | -1.52% |  0.66% |  1.53%\n",
      "01:13:10 | 3 16 | 01-200 | -1.13% | -0.74% | -1.39%\n",
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      "01:21:29 | 4 16 | 00-000 |  3.51% |  2.89% |  3.23%\n",
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      "01:32:49 | 4 16 | 03-200 |  3.09% |  1.51% |  1.24%\n",
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      "01:36:04 | 4 16 | 04-150 |  4.08% |  3.04% |  2.14%\n",
      "01:36:52 | 4 16 | 04-200 |  5.12% |  3.99% |  5.60%\n",
      "01:37:42 | 5 16 | 00-000 |  0.24% | -1.03% | -2.45%\n",
      "01:38:30 | 5 16 | 00-050 |  1.23% |  1.50% | -0.24%\n",
      "01:39:19 | 5 16 | 00-100 |  0.69% |  1.35% |  1.38%\n",
      "01:40:04 | 5 16 | 00-150 |  0.77% |  0.35% |  0.38%\n",
      "01:40:52 | 5 16 | 00-200 |  1.66% | -0.05% | -2.30%\n",
      "01:41:40 | 5 16 | 01-000 |  0.64% |  1.55% |  2.58%\n",
      "01:42:26 | 5 16 | 01-050 |  0.33% | -0.63% | -1.24%\n",
      "01:43:15 | 5 16 | 01-100 |  0.95% | -0.45% | -0.18%\n",
      "01:44:04 | 5 16 | 01-150 |  0.32% |  0.89% |  0.99%\n",
      "01:44:52 | 5 16 | 01-200 | -0.47% |  1.01% |  2.13%\n",
      "01:45:46 | 5 16 | 02-000 | -0.13% |  0.07% | -0.15%\n",
      "01:46:33 | 5 16 | 02-050 |  1.01% |  0.80% |  1.04%\n",
      "01:47:18 | 5 16 | 02-100 | -2.23% | -0.88% | -1.81%\n",
      "01:48:04 | 5 16 | 02-150 | -0.53% | -0.40% |  0.36%\n",
      "01:48:48 | 5 16 | 02-200 | -1.01% | -0.69% | -1.00%\n",
      "01:49:34 | 5 16 | 03-000 |  2.08% |  0.63% | -2.26%\n",
      "01:50:21 | 5 16 | 03-050 |  3.04% |  2.32% |  0.98%\n",
      "01:51:10 | 5 16 | 03-100 |  1.88% |  2.43% |  3.18%\n",
      "01:51:57 | 5 16 | 03-150 | -0.14% | -0.10% | -2.78%\n",
      "01:52:43 | 5 16 | 03-200 | -1.89% | -1.42% | -2.03%\n",
      "01:53:29 | 5 16 | 04-000 | -1.78% |  0.81% | -0.67%\n",
      "01:54:14 | 5 16 | 04-050 |  2.50% |  2.55% |  2.48%\n",
      "01:54:58 | 5 16 | 04-100 |  5.53% |  3.41% |  1.30%\n",
      "01:55:44 | 5 16 | 04-150 |  1.75% |  1.71% |  0.65%\n",
      "01:56:29 | 5 16 | 04-200 | -0.88% | -1.81% | -0.58%\n",
      "01:57:16 | 6 16 | 00-000 | -0.91% |  0.47% |  1.53%\n",
      "01:58:03 | 6 16 | 00-050 | -1.59% | -1.87% | -2.44%\n",
      "01:58:51 | 6 16 | 00-100 | -0.84% | -1.49% | -2.67%\n",
      "01:59:37 | 6 16 | 00-150 | -0.81% | -1.28% | -1.62%\n",
      "02:00:25 | 6 16 | 00-200 | -1.77% | -1.16% | -0.99%\n",
      "02:01:16 | 6 16 | 01-000 |  4.07% | -0.77% | -0.41%\n",
      "02:02:01 | 6 16 | 01-050 |  1.88% |  0.21% | -0.29%\n",
      "02:02:46 | 6 16 | 01-100 |  4.03% |  0.88% | -0.09%\n",
      "02:03:32 | 6 16 | 01-150 | -0.98% |  0.66% | -0.28%\n",
      "02:04:16 | 6 16 | 01-200 | -1.12% |  1.88% |  2.08%\n",
      "02:05:08 | 6 16 | 02-000 |  2.81% |  2.84% |  3.80%\n",
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      "02:06:40 | 6 16 | 02-100 |  1.78% |  2.04% |  0.55%\n",
      "02:07:28 | 6 16 | 02-150 | -0.90% | -0.86% | -0.76%\n",
      "02:08:16 | 6 16 | 02-200 | -1.65% | -0.77% | -1.83%\n",
      "02:09:02 | 6 16 | 03-000 |  2.50% |  2.39% |  2.61%\n",
      "02:09:46 | 6 16 | 03-050 |  4.88% | -0.28% | -0.86%\n",
      "02:10:31 | 6 16 | 03-100 | -0.59% |  0.01% |  0.53%\n",
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      "02:12:03 | 6 16 | 03-200 |  1.38% |  0.96% |  3.28%\n",
      "02:12:49 | 6 16 | 04-000 |  5.03% |  1.07% |  0.58%\n",
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      "02:14:19 | 6 16 | 04-100 | -4.61% | -1.05% | -1.75%\n",
      "02:15:04 | 6 16 | 04-150 |  1.91% |  1.22% |  0.39%\n",
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      "02:16:35 | 2 32 | 00-000 | -2.64% | -1.53% | -1.18%\n",
      "02:17:22 | 2 32 | 00-050 |  0.97% |  0.42% | -0.07%\n",
      "02:18:07 | 2 32 | 00-100 |  0.75% |  0.69% |  0.92%\n",
      "02:18:54 | 2 32 | 00-150 |  0.78% |  0.35% |  1.01%\n",
      "02:19:41 | 2 32 | 00-200 | -0.35% | -0.54% | -0.02%\n",
      "02:20:31 | 2 32 | 01-000 | -0.84% | -0.25% | -1.19%\n",
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      "02:24:27 | 2 32 | 02-000 | -1.29% | -1.24% | -1.16%\n",
      "02:25:17 | 2 32 | 02-050 |  1.91% |  1.92% |  3.36%\n",
      "02:26:05 | 2 32 | 02-100 |  1.27% |  1.14% |  1.07%\n",
      "02:26:54 | 2 32 | 02-150 | -1.82% | -1.97% | -2.28%\n",
      "02:27:40 | 2 32 | 02-200 |  3.32% |  3.34% |  4.54%\n",
      "02:28:26 | 2 32 | 03-000 |  2.63% |  2.00% |  1.17%\n",
      "02:29:13 | 2 32 | 03-050 |  0.90% |  0.52% | -0.24%\n",
      "02:29:58 | 2 32 | 03-100 |  0.40% | -0.58% | -0.35%\n",
      "02:30:44 | 2 32 | 03-150 | -1.13% | -1.98% | -0.92%\n",
      "02:31:31 | 2 32 | 03-200 | -1.14% | -1.46% | -0.85%\n",
      "02:32:19 | 2 32 | 04-000 | -4.14% | -1.87% | -1.54%\n",
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      "02:36:22 | 3 32 | 00-000 |  2.71% | -0.83% | -1.35%\n",
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      "02:38:40 | 3 32 | 00-150 |  0.70% |  0.50% | -0.08%\n",
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      "02:51:15 | 3 32 | 04-100 |  0.91% |  0.89% | -1.35%\n",
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      "02:52:05 | 3 32 | 04-200 | -2.51% | -1.11% | -2.91%\n",
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      "02:55:07 | 4 32 | 01-050 |  2.06% |  1.26% |  1.08%\n",
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      "02:57:33 | 4 32 | 02-100 | -0.96% | -0.99% | -1.45%\n",
      "02:57:58 | 4 32 | 02-150 |  0.12% |  0.18% | -1.14%\n",
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      "02:58:52 | 4 32 | 03-000 | -0.47% | -0.04% |  2.34%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "02:59:16 | 4 32 | 03-050 |  2.26% |  1.44% |  1.23%\n",
      "02:59:41 | 4 32 | 03-100 |  0.56% |  0.28% | -1.49%\n",
      "03:00:05 | 4 32 | 03-150 |  1.58% |  1.06% |  1.86%\n",
      "03:00:30 | 4 32 | 03-200 |  4.93% |  1.76% | -0.40%\n",
      "03:00:55 | 4 32 | 04-000 | -2.71% | -1.63% | -1.81%\n",
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      "03:02:58 | 5 32 | 00-000 |  2.18% |  1.41% |  1.91%\n",
      "03:03:23 | 5 32 | 00-050 | -0.11% |  1.47% |  1.79%\n",
      "03:03:48 | 5 32 | 00-100 |  0.35% |  1.34% |  1.53%\n",
      "03:04:12 | 5 32 | 00-150 |  0.33% |  0.65% |  0.39%\n",
      "03:04:36 | 5 32 | 00-200 |  0.55% | -0.31% | -0.22%\n",
      "03:05:01 | 5 32 | 01-000 |  2.43% |  1.36% |  1.22%\n",
      "03:05:26 | 5 32 | 01-050 | -0.20% | -0.08% |  0.09%\n",
      "03:05:54 | 5 32 | 01-100 |  2.05% |  2.33% |  2.85%\n",
      "03:06:18 | 5 32 | 01-150 |  0.26% |  0.21% | -0.57%\n",
      "03:06:43 | 5 32 | 01-200 |  1.97% |  0.95% |  0.98%\n",
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      "03:07:32 | 5 32 | 02-050 | -0.23% | -0.67% | -1.29%\n",
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      "03:08:22 | 5 32 | 02-150 | -2.70% | -2.80% | -3.16%\n",
      "03:08:46 | 5 32 | 02-200 |  0.92% |  0.11% | -0.03%\n",
      "03:09:11 | 5 32 | 03-000 | -0.33% | -1.54% |  0.21%\n",
      "03:09:35 | 5 32 | 03-050 | -1.45% | -0.91% | -1.54%\n",
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      "03:10:25 | 5 32 | 03-150 |  1.14% | -0.11% |  0.57%\n",
      "03:10:49 | 5 32 | 03-200 | -1.10% | -1.20% | -2.25%\n",
      "03:11:16 | 5 32 | 04-000 | -2.35% |  0.78% |  0.82%\n",
      "03:11:40 | 5 32 | 04-050 |  1.28% | -1.96% | -2.38%\n",
      "03:12:05 | 5 32 | 04-100 |  3.62% | -1.04% | -1.34%\n",
      "03:12:29 | 5 32 | 04-150 |  0.28% | -1.08% |  0.13%\n",
      "03:12:53 | 5 32 | 04-200 |  4.91% |  1.66% |  2.77%\n",
      "03:13:18 | 6 32 | 00-000 | -1.36% | -3.17% | -2.66%\n",
      "03:13:44 | 6 32 | 00-050 | -1.78% | -1.05% | -1.28%\n",
      "03:14:10 | 6 32 | 00-100 | -0.76% | -1.08% | -1.79%\n",
      "03:14:34 | 6 32 | 00-150 | -1.32% | -1.27% | -1.29%\n",
      "03:14:58 | 6 32 | 00-200 | -1.08% | -0.61% | -0.72%\n",
      "03:15:23 | 6 32 | 01-000 |  3.12% |  0.95% |  1.79%\n",
      "03:15:47 | 6 32 | 01-050 |  0.06% | -1.50% | -1.52%\n",
      "03:16:15 | 6 32 | 01-100 | -1.11% | -1.48% | -2.39%\n",
      "03:16:42 | 6 32 | 01-150 |  1.85% | -0.63% | -0.87%\n",
      "03:17:06 | 6 32 | 01-200 | -0.10% | -1.60% | -0.99%\n",
      "03:17:33 | 6 32 | 02-000 |  1.16% |  0.97% |  2.08%\n",
      "03:17:57 | 6 32 | 02-050 |  0.64% |  0.25% | -1.16%\n",
      "03:18:21 | 6 32 | 02-100 |  0.48% |  0.63% |  1.35%\n",
      "03:18:45 | 6 32 | 02-150 | -0.18% | -0.55% | -0.45%\n",
      "03:19:10 | 6 32 | 02-200 |  0.05% |  0.27% | -0.42%\n",
      "03:19:35 | 6 32 | 03-000 |  0.41% |  0.18% |  1.20%\n",
      "03:19:59 | 6 32 | 03-050 |  2.24% |  1.90% |  1.98%\n",
      "03:20:23 | 6 32 | 03-100 |  0.65% |  1.06% |  1.61%\n",
      "03:20:48 | 6 32 | 03-150 | -2.33% |  1.44% |  1.27%\n",
      "03:21:12 | 6 32 | 03-200 | -0.20% |  0.42% | -0.42%\n",
      "03:21:38 | 6 32 | 04-000 | -4.18% | -0.86% | -1.21%\n",
      "03:22:04 | 6 32 | 04-050 |  3.33% |  3.42% |  2.74%\n",
      "03:22:27 | 6 32 | 04-100 | -1.67% | -2.63% | -1.03%\n",
      "03:22:51 | 6 32 | 04-150 | -0.61% | -0.45% | -1.25%\n",
      "03:23:15 | 6 32 | 04-200 | -3.89% | -2.30% | -2.11%\n"
     ]
    }
   ],
   "source": [
    "start = time()\n",
    "for units, n_factors in param_grid:\n",
    "    scores = []\n",
    "    model = make_model(hidden_units=units, n_factors=n_factors)\n",
    "    for fold, (train_idx, val_idx) in enumerate(cv.split(data)):\n",
    "        X1_train, X2_train, y_train, X1_val, X2_val, y_val = get_train_valid_data(data,\n",
    "                                                                                  train_idx,\n",
    "                                                                                  val_idx)\n",
    "        for epoch in range(250):\n",
    "            model.fit([X1_train, X2_train], y_train,\n",
    "                      batch_size=batch_size,\n",
    "                      validation_data=([X1_val, X2_val], y_val),\n",
    "                      epochs=epoch + 1,\n",
    "                      initial_epoch=epoch,\n",
    "                      verbose=0, shuffle=True)\n",
    "            result = (pd.DataFrame({'y_pred': model.predict([X1_val,\n",
    "                                                             X2_val]).reshape(-1),\n",
    "                                    'y_true': y_val.stack().values},\n",
    "                                   index=y_val.stack().index)\n",
    "                      .replace(-2, np.nan).dropna())\n",
    "            r0 = spearmanr(result.y_true, result.y_pred)[0]\n",
    "            r1 = result.groupby(level='date').apply(lambda x: spearmanr(x.y_pred,\n",
    "                                                                        x.y_true)[0])\n",
    "\n",
    "            scores.append([units, n_factors, fold, epoch, r0,\n",
    "                           r1.mean(), r1.std(), r1.median()])\n",
    "            if epoch % 50 == 0:\n",
    "                print(f'{format_time(time()-start)} | {n_factors} {units:02} | {fold:02}-{epoch:03} | {r0:6.2%} | '\n",
    "                  f'{r1.mean():6.2%} | {r1.median():6.2%}')            \n",
    "    scores = pd.DataFrame(scores, columns=cols)\n",
    "    scores.to_hdf(results_path / 'scores.h5', f'{units}/{n_factors}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T22:38:05.473536Z",
     "start_time": "2020-06-22T22:38:05.347146Z"
    }
   },
   "outputs": [],
   "source": [
    "scores = []\n",
    "with pd.HDFStore(results_path / 'scores.h5') as store:\n",
    "    for key in store.keys():\n",
    "        scores.append(store[key])\n",
    "scores = pd.concat(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T22:38:05.528268Z",
     "start_time": "2020-06-22T22:38:05.522598Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 18750 entries, 0 to 1249\n",
      "Data columns (total 8 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   units            18750 non-null  int64  \n",
      " 1   n_factors        18750 non-null  int64  \n",
      " 2   fold             18750 non-null  int64  \n",
      " 3   epoch            18750 non-null  int64  \n",
      " 4   ic_mean          18750 non-null  float64\n",
      " 5   ic_daily_mean    18750 non-null  float64\n",
      " 6   ic_daily_std     18750 non-null  float64\n",
      " 7   ic_daily_median  18750 non-null  float64\n",
      "dtypes: float64(4), int64(4)\n",
      "memory usage: 1.3 MB\n"
     ]
    }
   ],
   "source": [
    "scores.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T22:38:08.227517Z",
     "start_time": "2020-06-22T22:38:08.216496Z"
    }
   },
   "outputs": [],
   "source": [
    "avg = (scores.groupby(['n_factors', 'units', 'epoch'])\n",
    "       ['ic_mean', 'ic_daily_mean', 'ic_daily_median']\n",
    "       .mean()\n",
    "      .reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T22:38:31.991665Z",
     "start_time": "2020-06-22T22:38:31.952196Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>n_factors</th>\n",
       "      <th>units</th>\n",
       "      <th>epoch</th>\n",
       "      <th>ic_mean</th>\n",
       "      <th>ic_daily_mean</th>\n",
       "      <th>ic_daily_median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_factors</th>\n",
       "      <th>units</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">5</th>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>162</td>\n",
       "      <td>0.046922</td>\n",
       "      <td>0.029146</td>\n",
       "      <td>0.034003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>141</td>\n",
       "      <td>0.058828</td>\n",
       "      <td>0.029581</td>\n",
       "      <td>0.031755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>74</td>\n",
       "      <td>0.042003</td>\n",
       "      <td>0.026353</td>\n",
       "      <td>0.030491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0.078509</td>\n",
       "      <td>0.034448</td>\n",
       "      <td>0.030237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>131</td>\n",
       "      <td>0.038715</td>\n",
       "      <td>0.024700</td>\n",
       "      <td>0.029099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>151</td>\n",
       "      <td>0.017604</td>\n",
       "      <td>0.023406</td>\n",
       "      <td>0.029056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0.030489</td>\n",
       "      <td>0.025923</td>\n",
       "      <td>0.028632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>0.018523</td>\n",
       "      <td>0.018433</td>\n",
       "      <td>0.026335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0.027900</td>\n",
       "      <td>0.023967</td>\n",
       "      <td>0.024697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>16</th>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>71</td>\n",
       "      <td>0.014876</td>\n",
       "      <td>0.013128</td>\n",
       "      <td>0.023805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 n_factors  units  epoch   ic_mean  ic_daily_mean  \\\n",
       "n_factors units                                                     \n",
       "5         8              5      8    162  0.046922       0.029146   \n",
       "          8              5      8    141  0.058828       0.029581   \n",
       "          8              5      8     74  0.042003       0.026353   \n",
       "          8              5      8      2  0.078509       0.034448   \n",
       "          8              5      8    131  0.038715       0.024700   \n",
       "2         16             2     16    151  0.017604       0.023406   \n",
       "4         8              4      8      3  0.030489       0.025923   \n",
       "          8              4      8     14  0.018523       0.018433   \n",
       "          8              4      8      6  0.027900       0.023967   \n",
       "6         16             6     16     71  0.014876       0.013128   \n",
       "\n",
       "                 ic_daily_median  \n",
       "n_factors units                   \n",
       "5         8             0.034003  \n",
       "          8             0.031755  \n",
       "          8             0.030491  \n",
       "          8             0.030237  \n",
       "          8             0.029099  \n",
       "2         16            0.029056  \n",
       "4         8             0.028632  \n",
       "          8             0.026335  \n",
       "          8             0.024697  \n",
       "6         16            0.023805  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top = (avg.groupby(['n_factors', 'units'])\n",
    "       .apply(lambda x: x.nlargest(n=5, columns=['ic_daily_median']))\n",
    "       .reset_index(-1, drop=True))\n",
    "\n",
    "top.nlargest(n=5, columns=['ic_daily_median'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T22:52:08.209797Z",
     "start_time": "2020-06-22T22:52:08.200596Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_factors</th>\n",
       "      <th>units</th>\n",
       "      <th>epoch</th>\n",
       "      <th>ic_mean</th>\n",
       "      <th>ic_daily_mean</th>\n",
       "      <th>ic_daily_median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2412</th>\n",
       "      <td>5</td>\n",
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       "      <td>162</td>\n",
       "      <td>0.046922</td>\n",
       "      <td>0.029146</td>\n",
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       "    <tr>\n",
       "      <th>2391</th>\n",
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       "      <td>8</td>\n",
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       "      <td>0.029581</td>\n",
       "      <td>0.031755</td>\n",
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       "    <tr>\n",
       "      <th>2324</th>\n",
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       "      <td>0.026353</td>\n",
       "      <td>0.030491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2252</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0.078509</td>\n",
       "      <td>0.034448</td>\n",
       "      <td>0.030237</td>\n",
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       "    <tr>\n",
       "      <th>2381</th>\n",
       "      <td>5</td>\n",
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       "      <td>0.024700</td>\n",
       "      <td>0.029099</td>\n",
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       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>151</td>\n",
       "      <td>0.017604</td>\n",
       "      <td>0.023406</td>\n",
       "      <td>0.029056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2383</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>133</td>\n",
       "      <td>0.029274</td>\n",
       "      <td>0.024345</td>\n",
       "      <td>0.028807</td>\n",
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       "    <tr>\n",
       "      <th>1503</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0.030489</td>\n",
       "      <td>0.025923</td>\n",
       "      <td>0.028632</td>\n",
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       "    <tr>\n",
       "      <th>2306</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>56</td>\n",
       "      <td>0.056498</td>\n",
       "      <td>0.031930</td>\n",
       "      <td>0.027303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2386</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>136</td>\n",
       "      <td>0.036864</td>\n",
       "      <td>0.025605</td>\n",
       "      <td>0.026759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1514</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>0.018523</td>\n",
       "      <td>0.018433</td>\n",
       "      <td>0.026335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>103</td>\n",
       "      <td>0.041208</td>\n",
       "      <td>0.024088</td>\n",
       "      <td>0.025368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2271</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>0.058964</td>\n",
       "      <td>0.032550</td>\n",
       "      <td>0.025107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2418</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>168</td>\n",
       "      <td>0.034421</td>\n",
       "      <td>0.022460</td>\n",
       "      <td>0.025065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2392</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>142</td>\n",
       "      <td>0.035092</td>\n",
       "      <td>0.023596</td>\n",
       "      <td>0.024735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1506</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0.027900</td>\n",
       "      <td>0.023967</td>\n",
       "      <td>0.024697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2256</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0.039973</td>\n",
       "      <td>0.017784</td>\n",
       "      <td>0.024316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2372</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>122</td>\n",
       "      <td>0.029645</td>\n",
       "      <td>0.019522</td>\n",
       "      <td>0.023960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3321</th>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>71</td>\n",
       "      <td>0.014876</td>\n",
       "      <td>0.013128</td>\n",
       "      <td>0.023805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.054724</td>\n",
       "      <td>0.026102</td>\n",
       "      <td>0.023567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      n_factors  units  epoch   ic_mean  ic_daily_mean  ic_daily_median\n",
       "2412          5      8    162  0.046922       0.029146         0.034003\n",
       "2391          5      8    141  0.058828       0.029581         0.031755\n",
       "2324          5      8     74  0.042003       0.026353         0.030491\n",
       "2252          5      8      2  0.078509       0.034448         0.030237\n",
       "2381          5      8    131  0.038715       0.024700         0.029099\n",
       "401           2     16    151  0.017604       0.023406         0.029056\n",
       "2383          5      8    133  0.029274       0.024345         0.028807\n",
       "1503          4      8      3  0.030489       0.025923         0.028632\n",
       "2306          5      8     56  0.056498       0.031930         0.027303\n",
       "2386          5      8    136  0.036864       0.025605         0.026759\n",
       "1514          4      8     14  0.018523       0.018433         0.026335\n",
       "2353          5      8    103  0.041208       0.024088         0.025368\n",
       "2271          5      8     21  0.058964       0.032550         0.025107\n",
       "2418          5      8    168  0.034421       0.022460         0.025065\n",
       "2392          5      8    142  0.035092       0.023596         0.024735\n",
       "1506          4      8      6  0.027900       0.023967         0.024697\n",
       "2256          5      8      6  0.039973       0.017784         0.024316\n",
       "2372          5      8    122  0.029645       0.019522         0.023960\n",
       "3321          6     16     71  0.014876       0.013128         0.023805\n",
       "2250          5      8      0  0.054724       0.026102         0.023567"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg.nlargest(n=20, columns=['ic_daily_median'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T22:36:36.837952Z",
     "start_time": "2020-06-22T22:36:32.603284Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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s1vvmyiuvJDAwkEmTJrFr1y4iIiIoKyuzec2fHzyYm5vLww8/bA2rTy2tUZO69PHVV19l6tSp3H///QC0b9+eCRMmMHv27CoPEmzevPkZP5QSERERuZgpeBYREZGLXk1fcT9VamoqQUFBNiHSqUFhTk4OTk5OVVZzVoaFZrOZjh07snz5cpYuXcoHH3zAsmXLCA4OZvr06Vx//fW0bNnytPufffZZPvnkE2vbvXr1YuXKlUDFqtWQkBC+/PJLOnXqxJdffomzszM33HCD9fh///vfvPPOO+Tl5REaGkqPHj1wcnKqscbzqXJzc601rk8VHBxs8/O5nKPyPJXX7M/XMT8/32bbqUFc5e/lTB8eREREWFc2m0wmPDw8aNmypfXhbwDZ2dkUFhbaBLyVTj0OqLGsw6njCQwMrLI9MDDQZrVudccA1a70/XMAeaotW7Ywe/Zs4uLi8Pf3Jzw8HDc3tyrX5c+BqYODg/V3lJOTg7OzMz4+PtWeo7KcSeWq6j/LysqyCY0rmc3mGvuel5fHhAkT2LNnD3PmzLF5OKOXl1eV3z1U1OH28vKqtr3qVLfCvnK1+uHDh4mNjbXeG5VOLe+SnJxsDZ2XLFliE95X18fKn+vSx5CQEFauXEl6ejp5eXm0adPGWkv91BrTUHEfnKlMiIiIiMjFTMGziIiIXPQCAgLo0qULP/zwA1OmTKn2mL/+9a8EBQXV+JV8X19fSktLyc7OtgmfKx8iV7ktIiKCt956i8LCQrZv384777zDk08+ydatW2natOlp9z/22GPce++91rZPDSUdHBy45ZZb+OKLL5g0aRKff/45N910k7WW7Keffsr8+fOZPn06w4YNw9vbG6gIrGvDz8+PjIyMKttPral8rueAiusYHR1dZXtaWtppSzTUlre3N127dj3jMYGBgbz11lvnfD5fX99qr1t6ejrt27c/5/ZPdfLkSZ588kluv/12PvjgA+sq8CeffLLKgydPx9vbm5KSEvLy8qy/Q4C9e/fi4+ODt7c3JpOJjz76qMqKYKDaDyig4h6qboVuZmYmDzzwACdPnmTBggUMHjzYZn+bNm2sdY4rZWVlYTabadu2ba3GVFpaymeffUanTp1sAuiioiJrn/v27cvHH39c7etjYmK4//77KSws5L333rNZbV9TH+Pi4gCsdaDPxDAMNm7cSJcuXWjXrp31A5gjR47g4+Nj8+0CqPhQ40wr7kVEREQuZnq4oIiIiFwS7r//fg4fPmx9EOCp1q9fz/Hjx7nllltqfH1ERAQAX331lc32L774gsDAQNq0acN//vMfBg8eTElJCe7u7gwaNIiJEydSVlZGSkrKGfe3aNGCrl27Wv/9OdC6/fbbiY2N5aeffmLPnj029Zf37t1LSEgIo0ePtoaJBw8eJDMzs1arkXv37k1eXh7bt2+3bjtx4oTNw/Zqe47TlR6IiIjg+PHjNkGpxWJh8+bN9OzZ84z9tIeIiAgyMzPx8PCwud4bNmzgs88+q3NbW7ZssSk1EhUVxdGjR+0+nkOHDlFSUsL48eOtoXNBQQG7d++u9YpzqKj/DNg8dLKyDMb69euJiIjAMAzy8/Ntrs/27dtZvnx5tWE0VKzmraynXqmkpIS//e1vxMXF8e6771YJnQGuvvpqDhw4YPPazZs34+zszFVXXVWrMTk5ObFw4UJrjfBKX3/9Nc7OznTv3h1/f3+b8VR+QJGbm8uDDz5IaWkpq1atqhI6V/Zx27ZtNg+M3Lx5M35+fnTq1KlWfTSZTCxcuJAPP/zQui0nJ4fPP/+cgQMH2rxvDMMgNTX1tKVLRERERC52WvEsIiIil4Thw4fz7bffMm3aNPbv38/gwYMxmUz8+OOPfPTRR/zlL3/hjjvuqPH1nTp14sYbb2TOnDnk5+cTFhbGli1b2LhxI9OmTcPBwYErr7yStLQ0nnzySe655x5KSkpYsmQJLVq0oHPnznh7e592/5m0b9+erl27MmPGDEJDQ20eOta1a1dWr17NokWL6NWrF1FRUSxevBiTyWRd9Xk6/fr146qrrmLq1KlMmTIFDw8P5s+fb1N6orbn8PHxYffu3Vx55ZVVag2PGDGC999/n4cffpiJEyfi7e3N8uXLSU9P55FHHjljP+3huuuuo2vXrowfP57HHnuMZs2a8fXXX7Nq1SpefPHFOrX1yCOPMGrUKB5++GEeeOAB8vLymD9/PqGhodYH99lL586dcXR05NVXX2X06NFkZWWxbNky0tPTq625XJPLL7+c6667jpkzZ2I2m2ndujWrV6+msLCQkSNHEhoayo033sjUqVN57LHHaN++PTt27GDJkiU89NBDNX6w0KdPH959912Sk5OtwfiqVavYv38/Dz/8MM7Ozuzbt896vI+PD+3atWPYsGHWtp988klSU1N59dVXufvuu6uUejmdRx55hGnTpjFz5kwGDRrEb7/9xuLFixkzZkyV1cSnqnwA4bPPPovZbLbpY1BQEC1atOCee+7hgw8+YPz48YwbN47Dhw+zdOlSJk+eXKdrP3r0aP71r3/Rtm1bWrVqxeLFiykqKuLRRx+1OS46Oprc3Fz69etX67ZFRERELjYKnkVEROSSYDKZmDdvHmvXrmXdunV8/fXXWCwW2rZty/PPP8+dd95Z7QPOTvXaa6/xxhtvsHz5crKzs2nXrh2vvvoqt956KwBt27bl3//+NwsWLOCJJ54AKlYSv/baazg7O59xf23cfvvtzJgxg8cff9ymvyNGjODEiROsXr2ad955h9DQUMaNG0dUVBS7d++u1fVZsmQJL7/8MrNmzcLJyYkHH3yQTZs21fkcjz32GPPnz2fXrl1s27bN5jxeXl6sWrWKuXPnMmPGDMrKyujevTurVq2qtkZvfXB0dOTdd9/ltdde49VXX7WGr7Nnz7ZZRV4b4eHhvP/++8ybN48nn3wSd3d3Bg4cyNSpU+tU+7c22rZty9y5c1m0aBHjx48nODiYAQMGcMcddzBjxgxSUlKqrb1cnddff51//etfLF68mIKCAsLDw1m+fLk1oK2815cuXUpGRgahoaFMnjyZcePG1dhm79698fX15ccff+TOO+8EKmpSA7z99tu8/fbbNsdfe+21vPXWW7i7u/Pee+8xY8YMpkyZgre3N6NHj+Yf//hHna7PyJEjcXZ25r333mPt2rUEBQUxYcIExo8ff9rXVT5Q8uWXX66y795772XatGk0adKE9957j1mzZvHEE08QFBTExIkTT3s9qjN27Fjy8/N5++23yc3NpXv37qxYsaJKSZEff/yRkJAQm4dPioiIiFxqTEZdvrcnIiIiIiKN1sKFC9m2bRsfffRRQ3flonbrrbcyYsQIHnjggYbuioiIiEi9UY1nERERERGplfvvv5/Y2Fh+/fXXhu7KRWvbtm2YzWZGjRrV0F0RERERqVcKnkVEREREpFZ8fHx48cUXefXVVxu6KxclwzB47bXXmDVrFm5ubg3dHREREZF6pVIbIiIiIiIiIiIiImJXWvEsIiIiIiIiIiIiInal4FlERERERERERERE7ErBs4iIiIiIiIiIiIjYlYJnEREREREREREREbErBc8iIiIiIiIiIiIiYlcKnkVERERERERERETErhQ8i4iIiIiIiIiIiIhdKXgWEREREREREREREbtS8CwiIiIiIiIiIiIidqXgWURERERERERERETsSsGziIiIiIiIiIiIiNiVgmcRERERERERERERsSsFzyIiIiIiIiIiIiJiVwqeRURERERERERERMSuFDyLiIiIiIiIiIiIiF0peBYRERERERERERERu3Jq6A6IVGf9+vW8++67mEwm3N3dee655+jatWuV48LCwujYsSMODn98hhIeHs6sWbPqfM68vDweffRRVqxYcU59F5ELywcffMBHH32EyWSiZcuWzJw5k8DAwCrHaT4RkdravHkzU6dOZe/evdXu13wiImcyZ84cvvrqK3x9fQFo27Yt8+fPr3Kc5hMROZMjR44wc+ZM8vLycHBwYMaMGYSHh1c5TvOJNAQFz3LBiY6O5tVXX2XdunU0adKE7777jscff5xvv/222uPff/99AgICzvm8OTk5/Pbbb+fcjohcOA4cOMCyZctYv3493t7ezJ07lzfeeIMZM2ZUe7zmExE5k5MnTzJ37twzHqf5REROZ+/evcybN4+ePXue8VjNJyJSk8LCQsaNG8esWbMYOHAgmzdvZsqUKXz11VfVHq/5RM43Bc9ywXFxcWHmzJk0adIEqPgELj09HYvFgouLS63b+fjjj1mzZg0lJSXk5OTw8MMPc8899wDw1ltv8cknn+Dk5ETr1q2ZM2cOzzzzDEVFRQwfPpx169axd+9eXnnlFQoLC3F2dmbixIkMGDCAdevW8fHHH1NYWIiXlxfz5s3jqaeeIisrC4CBAwcyceJE+18YEamz8PBw/ve//+Hs7ExxcTEpKSm0aNGizu1oPhERqPjjburUqTz99NNMmTLlrNrQfCIiFouFQ4cO8c477xAXF0ebNm145plnaN68eZ3a0XwiIj/99BMtW7Zk4MCBAAwePFh/78iFxRC5gJWXlxuTJ082Hn/88Wr3d+zY0Rg2bJhx6623Wv+lp6cbZrPZuPvuu43MzEzDMAxj7969Rvfu3Q3DMIzNmzcbN9xwg5GdnW0YhmG8/PLLxptvvmnExcVZj8nMzDT69Olj7Nu3zzAMwzh69KjRq1cvIzY21vjvf/9rXHXVVUZeXp5hGIaxaNEi45///KdhGIaRn59vTJw40cjNza2/iyIidbZp0yajV69eRv/+/Y0TJ05Ue4zmExE5kylTphj/+c9/bN7j1dF8IiKnExsbazz00EPGkSNHjPLycuPtt982hg8fbpSXl1c5VvOJiJzO0qVLjccff9x45plnjNtvv924//77jQMHDlR7rOYTaQha8SwXrIKCAp5++mmSk5N55513ajyupq+K/Pvf/+a7777j5MmTHD58mIKCAgC2b9/OTTfdZK2n9swzzwAQHx9vfe3+/ftp1aoV3bp1A+Cyyy6jZ8+e7NixA5PJRFhYGF5eXgBcc801jB8/nqSkJPr27cvkyZPx9va2z0UQEbsYMmQIQ4YMYe3atYwbN45NmzbZ1DarpPlERGqyatUqnJycuPPOO23e4zXRfCIiNWnZsiVvv/229edx48bx5ptvEh8fT8uWLascr/lERGpSWlrKd999x4oVK+jWrRubN29m/PjxbN26tdpvjGs+kfOt6l/dIheAxMRERo0ahaOjIytWrMDHx6dOr09OTua2224jISGBiIgIm69uODo6YjKZrD/n5uZW+QOyrKzM5hgAwzAoLS0FwMPDw7r9iiuuYMuWLYwcOZKEhATuuusuDhw4UKf+ikj9iImJYdeuXdaf77jjDhITE8nJyal1G5pPRATgk08+4bfffmP48OGMHz/e+vXSlJSUWreh+UREAA4fPsynn35qs80wDJydnWvdhuYTEQFo0qQJ7du3t4a+Q4YMoaysjLi4uFq3oflE6pOCZ7ngmM1mxowZww033MDrr7+Om5tbnds4cOAAAQEBTJgwgf79+7N161agYkLs27cvmzZtwmw2A7Bw4UKWL1+Ok5MTZWVlGIZB9+7diY6OZv/+/QAcO3aMnTt30qtXryrneu2113jzzTcZMmQIzz33HB06dODYsWPncAVExF7S0tL4xz/+QWZmJgAbNmzgsssuw9/fv9ZtaD4REaioffj555+zfv16li5dipubG+vXr6dp06a1bkPziYgAODg4MGvWLGsw9OGHHxIWFkZISEit29B8IiIAAwYMID4+3hre7ty5E5PJVKc6z5pPpD6p1IZccFatWkViYiKbNm1i06ZN1u3Lly+vdVjUr18/Pv74Y2666SZMJhO9evUiICCAmJgYBg4cyPHjxxk9ejQAHTp04KWXXsLd3Z0rrriCm2++mVWrVvHGG2/w0ksvUVRUhMlkYvbs2bRt25a9e/fanOv+++/n6aefZtiwYbi4uBAWFsbNN99svwsiImftyiuv5JFHHmHs2LE4OjrSpEkTFi9eXKc2NJ+IiL1oPhERgI4dO/L888/z97//nbKyMkJCQpg3b16d2tB8IoAwdb0AACAASURBVCIAwcHBLF68mBdffJHCwkJcXFxYuHAhrq6utW5D84nUJ5NhGEZDd0JERERERERERERELh0qtSEiIiIiIiIiIiIidqXgWURERERERERERETsSsGziIiIiIiIiIiIiNiVgmcRERERERERERERsSsFzyIiIiIiIiIiIiJiV04N3YHzZd++fQC4uro2cE8aRnFxcaMce2MdNzTesdd13MXFxXTv3r1O59B8onursWmsY9d8Uv90bzU+jXXs52M+AdizZw/u7u51ft2lQPdW46Ox1++covmk8d1bjXXcoLHX53zSaILnyovYuXPnBu5Jw4iMjGyUY2+s44bGO/a6jjsyMrLO59B8onursWmsY9d8Uv90bzU+jXXs52M+ATCZTI3y+oLurcZIY6/fOUXzSeMbe2MdN2js9TmfqNSGiIiIiIiIiIiIiNiVgmcRERERERERERERsSsFzyIiIiIiIiIiIiJiVwqeRURERERERERERMSuFDyLiIiIiIiIiIiIiF0peBYRERERERERERERu1LwLCIiIiIiIiIiIiJ2peBZREREREREREREROxKwbOIiIiIiEgjs3pHLIZhnNVrF2w5Rln52b1WREREGg8FzyIiIiIiIo3My19Ekl1QUufXmYtLmbfpKGl5xfXQKxEREbmUKHgWERERERFpZJr7uZOQXVjn1yX9/pqE7AJ7d0lEREQuMQqeRUREREREGplQP3cSzyJ4TswpAiAhu8jeXRIREZFLjIJnERERERGRRqaZnxtJOXUPj5Nzfl/xnFX30FpEREQaFwXPIiIiIiIijUzzs13xnF2Er7vzWb1WREREGhcFzyIiIiIiIo1M6NnWeM4pJKK1PwnZhRxOzmV3TFY99E5EREQuBQqeRUREREREGplmvhUrnmMy8jmZnl/tMeXlRpVtSTlFRLT2JzG7kDe3RvH3D3aTV1RS390VERGRi5CCZxERERERkUamuZ8bidlFvPLVET74OabK/l+iMxi19Ocq25NyiriytT/xWYX8cCyNLs19mP7ZQXIVPouIiMifKHgWERERERFpZJr6uJGRX8yWwylkFVQNjb85nMq+uGwspeXWbYZhkJRdSKdmPhiGQbC3K/NHdqe0zGDQa99RVFJ2PocgIiIiFzgFzyIiIiIiIo2Ms6MDgZ6uOJhMZBVYquz/8Xg6mOBoSp51W15xKQA+bk4093Pn2rAm+Hm4sGB0DwI8nYlOq75kh4iIiDROCp5FREREREQaoZYB7ozoGUpGvm3wnGEuJjajgKHhIeyPz7FuT8ouopmfOyaTiYEdg7m5azPrvnZBXkSnm89b30VEROTCp+BZRERERESkEVp8T08e7NeWrD8Fz9uiMujVNoCerf3ZH59t3R6bWUALf3cAnh/WhW4t/az72gV7EpWqFc8iIiLyBwXPIiIiIiIijVATHzcCvVyrBM8HEnLo2dqfrqG+NiueT6SbaRfkVW1b7YO14llERERsKXgWERERERFppHzcnCgsKbN5iGB8ViEtAzzo3MyH6HQzhZaKhwaeSM+nbbBnte20C/ZUjWcRERGxoeBZRERERESkkTKZTPh5uNg8YDAuq6KkhpuzI51CfNgblwVAdFo+7YJqCp69iE4zsyUyhW8Op5yXvouIiMiFrcGD5/LycqZNm8bIkSMZM2YMMTExNvu/+eYb7rjjDkaOHMnatWtt9mVkZDBw4ECioqLOZ5dFREREREQuGYGeLmSeUm4jPquQlv4eAPRuG8DOE78Hz+n5tKthxbOvuzPuLk489uFeNvyaVP+dFhERkQtegwfPmzdvxmKxsGbNGiZPnsycOXOs+0pKSpg9ezbLli1j5cqVrFmzhrS0NOu+adOm4ebm1lBdFxERERERuej5ezpb6zznF5dSYCklyMsFgKvaBLDzZCZ5RSWYi0pp6l3z319hIV70bO1HTIZKboiIiMgFEDzv3r2ba665BoDu3btz4MAB676oqChatWqFr68vLi4uREREsGvXLgDmzp3LqFGjaNKkSYP0W0RERERE5FIQ4OlC5u+lNuKzCmnh74HJZALgyjb+7IvL5liqmTZBnjg4mGpsZ8l9Efzrru7EZBScl36LiIjIha3Bg2ez2YyX1x9PRnZ0dKS0tNS6z9vb27rP09MTs9nMunXrCAgIsAbWIiIiIiIicnYCPF2sK57jf6/vXMnPw4VQP3cWf3O8xvrOlXzcnGnq40qBpQxzcWm99llEREQufE4N3QEvLy/y8//4KlZ5eTlOTk7V7svPz8fb25uVK1diMpnYvn07kZGRPPXUUyxZsoTg4OAaz1NcXIxhGERGRtbfYC5gRUVFjXLsjXXc0HjHfj7GrflE91Zj01jHrvmk/uneanwa69jP17jLy8vP6jxlBbkcOZlHpF8hOyNz8KTEpp1Hr/Jh1b4surTwqFX7TT0d+HbXAdoHuNa5L2dL91bjo7HX79jPdj65FDTWe6uxjhs09voce4MHzz179mTr1q0MHTqUffv20bFjR+u+9u3bExMTQ3Z2Nh4eHuzatYtx48Zx0003WY8ZM2YML7zwwmlDZwBX14r/09O5c+f6GcgFLjIyslGOvbGOGxrv2Os67rOZYDWf6N5qbBrr2DWf1D/dW41PYx37+ZhPABwcHM7q+nbMPMHJ9HwCQ9tSGhXNFe1c6dy5vXV/Z+DW/rVv77Kd+Tj6NKVz52Z17svZ0r3V+Gjs9TunnO18cilorPdWYx03aOz1OZ80ePB8/fXX89NPPzFq1CgMw+Dll19mw4YNFBQUMHLkSJ5++mnGjRuHYRjccccdNG3atKG7LCIiIiIicskI8HThvZ9OsvLnGJwdHZh3d/dzaq91gAcnVedZRESk0Wvw4NnBwYEZM2bYbGvf/o9P1wcNGsSgQYNqfP3KlSvrrW8iIiIiIiKXugBPF5JyCvnw4atJyS2i/2VB59Re60APDiXl2ql3IiIicrFq8OBZREREREREGs7V7QJZ9/d+dG3ha5f2WgV68uWBZLu0JSIiIhcvh4bugIiIiIiIiDQcZ0cHu4XOAO2CPIlOyz/zgSIiInJJU/AsIiIiIiIidhPq505OYQnm4tKG7oqIiIg0IAXPIiIiIiIiYjcODibaBnkSlWq2W5uGYTB22Q4KLWV2a1NERETql4JnERERERERsasOTbw4bsfgOS6zkO+PppGSW2S3NkVERKR+KXgWERERERERu2of7EVUmv2C533x2QBkFljs1qaIiIjULwXPIiIiIiIiYlf2XvG8P64ieM7KV/AsIiJysVDwLCIiIiIiInbVoYkXx+244vnX+Gya+7qRoeBZRETkoqHgWUREREREROyqTZAH8VmFlJSVn3NbpWXlHEzMZUDHYK14FhERuYgoeBYRERERERG7cnVyJMjTheScc38YYHR6Pk193GgV6EGmgmcREZGLhoJnERERERERsbtmfu4k2SF4TsopormfG4GeLgqeRURELiIKnkVERERERMTumvm6kZRTeM7tpOcVE+zlir+HC1kFCp5FREQuFgqeRURERERExO6a+7mTmH3uK57TzcUEebkS4OmihwuKiIhcRBQ8i4iIiIiIiN3VdcXz9qgMPt+fWGV7Wl4xwd4VwbMeLihy4Xl4xS5yi0oauhsicgFS8CwiIiIiIiJ218y3biueP9+fyJtbo6ps14pnkQtbXGYBsRkFDd0NEbkAKXgWERERERERu2vuV7cVz4eScolMzq0SYKWZK1Y8+7g5U2Apo6Ss3N5dFZFzEOrnTmL2uddzF5FLj4JnERERERERsbtmvu4k5dRuxXNZucGR5DyGdm3G/w4m2+xLz7MQ5OWKg4MJfw9nPWBQ5ALTzM+t1u91EWlcFDyLiIiIiIiI3QV6umAuKqWopOyMx55INxPs7cqdPVuwOTLFZl+6uZggbxcA/D1cyFS5DZELSjNfdxLr8O0GEWk8FDyLiIiIiIiI3Tk4mGjq60pyLVZCHkzM5fLmPnRo4kV81h8BVmlZOTmFJQR6ugLg76ngWeRC09zPrU713EWk8VDwLCIiIiIiIvWiua878VmF5BSW8NmviTb7DMOw1nM+lJhLl2Y+vz9AsNh6TGa+BT8PZxwdTAAEe7uSmluMiFw4mvu6k6Qaz2clW6WD5BKn4FlERERERETqRd/2QfzvYDKrfonhiY/2sic2y7pvd0wW17/+HdujMvh8fxK92wXi4eKIYUCBpRSA1Lxigrxcra9pE+jByYz88z4OaZxKy8o5nmpu6G5c8Jr71b6eu/whM99Cz5c2MW75TvKLSxu6OyL1QsGziIiIiIiI1Is7r2zBhv2JfPhLLH/t14bnPzlAebkBQFxWAd5uzox++2du6dacq9oEYDKZCPR0IcNcsQow3VxMsPepwbMnJ9MVPMv5sTkyhfErdzV0Ny54TX3cSM0rouz39zZAfFZBA/bo4hCfVcBlTbzJyLewOybrzC8QuQgpeBYREREREZF6EernTrcWfni7OTNtWBdKy8utq54Ts4u4IyKU5X+9iqk3hllfE+jlaq3jnG62EHzKiud2wZ6cyFCgJefHLycyOZGej1mrUU/LxckBfw8XUvMqVj0bhsHQN37Qh0RnkJhdSOtAD8JDfTihayWXKAXPIiIiIiIiUm+e/ksnXhp+OSaTib+EN+PLA8kAJGQX0sLPnWvDmlhrOAM2dZ4TsgoJ8XWz7msT6MmJNDOGYSBS33acyMTD2ZHIpNyG7soFr5mfu/UBg9kFJeQWlXI4WdftdBKyi2ju507bIC+i01TSRS5NCp5FRERERESk3nRu5sOVbQIA+EvXEL46kIxhGCRmFxLq717l+FNLbRxJySUsxNu6L8DTBQPIKig5L32Xxiu3qIST6fkMu6I5v8XnNHR3Lngt/Nyt5TXifv/Pw8l5DdmlC15idiGhfu60C/IkWiue5RJVL8Hz0aNH2bFjB1FRUfXRvIiIiIiIiFyEwpp64+LkwG8JOSRkFdLcr2rwHODpYi21cTg5j04hPtZ9JpOJdkGe+lq6nDPDMPh0b0KNq+d3n8yiW0s/erTy40Cigucz6dzMm4OJFSuc4zILcXY0cUTB82klZhf+vuJZc5pcuuwWPFssFhYtWsSwYcOYNWsWa9asYc6cOdx66638+9//pqhITzgVERERERFpzEwmE9eFNeH7o2nW0OXPArwqgudCSxkJWYW0C/a02d8mSA8YPFeGYXA0pXGHgrGZBUxcs4/UvOJq9/9yIpNebQMID/XlYIJKRpxJeKivdWV4XFYBV7cLPKfgecKq3Ww6lGKv7p2T74+mVRnLz9EZDFv4A//38a9n3W7FHOhGC393UvOKKSopO9eu1klcZgHFpef3nNL42C14njZtGj169OCzzz7j/fff51//+hdvv/0269evp3PnzkybNs1epxIREREREZGLVN/2gXx1MBkHBxM+bs5V9gd6upCRb+FYah5tgzxxdrT9s7VNoFYHnq398dlEp5n59kgaQ9/4gfxG/NC87VEZABxLqb627o4TGfRqG8BlTb2Iycw/76HgxaZrqC8HEnMwDIO4zAIGdgwmIbvwrK7b7pgsvvgtmZiMC+N9vmJ7DN8dTbXZtnZnHG2DvDhwDh9KJGQXEernjpOjAy383Yk5zw9Ofej9XYxa+jOpuVooKvXHbsHznDlz6NevHw4Otk2aTCYGDhzIK6+8Yq9TiYiIiIiIyEWqV7sADiXmElrNameAQE9XMvMtHE7Oo3Mznyr7W/i7k5hdWN/dPCNzcSll5RfXQw7nfnWYh1fsYuE3x3ByNLE3Nruhu9Rgtkdn4OPmVO3K70JLGYeT8+jR0h9XJ0faBHrWGFBLhUAvV7xdnYjNLCDu928qtAn05HhqxXUrLSvnnrd/JqcW9dlf33SUdkGe1pI7DS063Uxmvm2/M/ItDOnchJiM/LN62GlRSRm5RSUEebkC/F5C6PzdY5bSck5k5HNNhyBuXfQT7/wQzcLtaXpwq9idk70bTEpK4vPPP6e4+I+vqzz22GP2Po2IiIiIiIhchHzcnOka6msNXP4swMuFDHMxh5Py6HTKgwUrNfN1Jymn4VfoTVi1h87NvHnmL50buiu1UlRSxt7YbPq0C+Roah5j+7Rhx8lM+l8W1NBdq9HJ9HxaBnjg6GCya7uGYbA9KoO7r2zJsdSqYd/e2Cy6NPPB3cURqHhAZmRyLl1b+Nq1H5ea8FBffkvIIT6zgJb+HoSFeHM4OY/wUF++PZLGtqgMotPN9GjlX2MbyTlF/JaQwz+u78jh5IYvcVJSVk5sRgHZBbYheGa+hTaBnrg4OZButhDsXf18VpPknCJCfNxw+P3ebnueHzB4MiOfFn7u/OOGMLq28OOL35L4MSaf+KxCWgZ4nLd+yKXP7g8XfPLJJzGbzQQFBVn/iYiIiIiIiFTq2yGIFv41rXh2IS2vmG+PpNKzddWAKsTXlZQG/mp4doGFPTFZrNkZd0Gsvq6NHScy6dzMhwWje7Dywd70aR/IzhOZDd2tGuUUlnDroh/58kCS3duOTs/HycHE4M5NOZ5adcVzZX3nSp1CvDmc1LhrYtdG11Bfth5OIyG7kBa/B89Hfg+PP9oRi7uzI/FZp3+/bDqUzKBOTWjq40qG2TbsLSopO+/fMojLLKC03Kiy+joz30KApwutAz2JzaxbYJyYXci6PfE093OzbmsV4EFc5vmbS46m5HFZUy8Aru/SlNdHdqdLEzd+S2jYB2kaRtVrLRc3uwfPnp6eTJo0iVGjRln/iYiIiIiIiFR69LoOTLq+Y7X7AjxdSMwpwt/ThSurDZ4rVjw35FfCN0em0q9DICOvask7P5w45/a2RaXXe6D24/F0+ncIwtPViTZBnkS09ufX+OwL9uFi7/10ApPJxPdH0/gtPoebF/zAQ+/vssvvPSrVTKdmPnRs6sXRFHOVNrdHZdC7XaD1507NfC6I1bcXutG9W3EgIQdvN2fcXRwrAvvkPFJzi9gVk8UdEaFnDJ6/PpTCDV2a4u/hQtafVhk/+8lv/GdXXH0OoYrotHw8XRzJ/r1ESOX75Y/g2YOT6bWvzbz8pxMMXfADsZkFPDH4Muv2lgEexGXWrcZzbtGZy5b82fFUM6/97whHU8x0bGr7jZIOAa4caODgeU9sNg+9v7NB+yD2Zffg+bLLLmPjxo1ER0dz4sQJTpw49/8RFhERERERkUuHl6sTfh4uNe5zdXLgicGXYTJVLbHg5eqEo4OJ3KKGezDeVweSuCk8hGs6BHMo6dyDmic+2ktkkv2DzZTcIr45nIKltJxNh1IY0PGPbyT7uDkzsGMwV83czNYjqadp5fwrKStn+baTvD6yGz8cS+et76MY3KkJ26LS7fJ7T84topmvG4Ferjg6mHj3xxPM/iKSrYdTyTAXE5mcS+9TVjx3DvEmMim3SkB9KDGXAkvjfUDjnwV5ubLmb1czZ0RXgN9XPOfx/bGKDz06NvUmLqvmcDWnoIS9sdkMDAsm0KviIaOniko1szsmq17H8GeVpUEyCyyk5RVz3avfUmgpo9ww8HBxpHWgJzG1DIwNw+D1zcf4+JE+zB/Vg77t/3g/VrRT+5XTeUUl9J/zDeV1/MDqaEoeb357nO+OpnHZn4PnQJcGX/GcmltEmrn4zAfKRcPuwXNkZCSrV69m+vTpTJs2jenTp9v7FCIiIiIiInKJMplMrJvQlwGnqT0c4utGcgPVeY5Oqwi/BnduSpuguq12rE5pWTkZ+ZZ6KdmxcX8SD6/YzaQ1+2gT6EHPP9XWXXJfBE8O6cjXB1Psfu5zkZxThLuzI9eFNcHBZGLr4VTGXdOOEB830vLO/feelFMRPAN0b+nHt0fS8HF3ZtLafXz4SywDLgvGzdnRenywtysmk4m0PNtAbOrHv/LtkbRz7s+lxM/DhSFdmgIQ6udOoaWMjfsT6dshkJb+Hqdd8bz1SCpXtwvAw8WpYsXzn4Ln2MwC9sefORgtLzfs9o2I6LR8erb2J7vAQmxmAYk5RcRk5hPo6YLJZKJ1gAcxGbULjOOzCnFzdqBDk6q160P93EnJKaakrJz404TzlVLzisktKiWzoG5lKXIKSyg34Ne4bDr+XmqjUodAVw4mVv2A5XzKLLBYV5fLpcHuDxdcuXKlzc8Wi2qziIiIiIiISO1d3vz0D3EL8XEjObeIsGoePng6BZZS/rs7ngJLGX8b2P6s+vavTUd56Jp2+Lg54+XiRHahhQJLKR4uZ/fndUa+BcOgXoLnoyl5XN+5Kb+cyOCzx/pXu4K8e0tf1u9LsPu5z0VSThHN/dwxmUxcGxZMaZmBr7szwd6upOQWVxvc1UVyThH9OlR8sLHsgaus27PyLby++Sivj+xuc7zJZKJTiDcHE3MJ9HIlIauQJj6uHEnOuyAedHmhMplMdAzxZuuRNKbfcnm1oepv8Tlc3twHBwcT/zuYzA2XhwAVAXZuUSll5cbv33AooaiknJjMfAospTg6mHB1cqzutEz77AA9W/kzomeLcx5DdFo+f+najDe3Hre+R/fH5RDgVfGNjTZBHqz4+fRBcbq5mKhUM1kFFsJrmNtcnBwI9nZlW1QGf1u5i0Mv3mR98GB1Kj8ESc4pqvFBrdXJKSzhxsubsuNEJm2DPG32BXpUfJskMaeIUL/qa/DXt0yzhbyiUkrLynFytPtaWWkAdv8trl69mhtvvJHBgwczaNAgbrnlltMeX15ezrRp0xg5ciRjxowhJibGZv8333zDHXfcwciRI1m7di0AZWVlPPPMM4waNYp7772X2NhYew9DRERERERELlBNfdxIOYvA7/1tMazbm8CCLcfIL659iYSE7EIKLWUcTzWz40Qmf+3XBgAHBxOtAs5t1XNqbkWAVB8B5pGUPB7s35Ydzw6heQ1BUudmPhxNyaOkrNzu5z9bidmF1hXJz93cmReHXw5AEx83Uu2y4vmP9k/1t4Ht6dDEi2vDmlTZ16OVH3tis9gSmcLIpds5lJRLablB0kXycMmGEhbiTaifO60DPQj1dychq9CmPMSYZb/w1cFkikrK+PFYOoM7VVx7RwcTPm5OZP++ojc2o4DWgR6EhfjwxuZj3PD69zWeMyajgAMJ5166pqzcIDI5l8ub++Du7MjRlIoHTP4an02AZ0XY2zrQk5Pp+addJfz90TT+sfZX9sfncHlozR+qtQxwZ+X2GIpKys9YbiLdfHbzRm5hCV1Dffn52cHVBvc9Wvo16ENHK8ur5BRq1fOlwu7B89q1a1m5ciUDBgxg9uzZtG9/+k+RN2/ejMViYc2aNUyePJk5c+ZY95WUlDB79myWLVvGypUrWbNmDWlpaWzduhWoCLmfeOIJZs+ebe9hiIiIiIiIyAWqma/bWQW1BxNzGHN1a8JDfdlxsvbhyuMf7mH+5qOs35fA8G7NbVY3twn05ER67Wuz/lllkJpg5wDTMAyOpZgJa+p92pWTHi5OtPD34Hiq2a7nPxeJOYXWoNzDxcla9qKJt6s1qD8XKbnFNPWpGjwHe7vy9aSB+Lo7V9l3ZesAdp3M4qfj6STlFLF6Ryzebk4k5WrF8+n0bOXP9V2aYjKZ8HBxwtvNyRqaWkrLyS4oYcGWY6zZGUeX5j4EnrJ6N8DThczfg8i4zAJaBnjQrYUvb30fTXxWYY0fHqXmFhOVdu7389GUPIK8XAnycsXP05lDibk4OZjYH59DoGfFiueKkhuQbq752/5ZBSUkZBeyZmcc4c19ajyuVYAH3xxOwWTijOU20q0rnus2b+QUluDj7lzjavFrOgbz/dGGKx9T+fvOqodyG7O/jGRXHeZ9sQ+7B8/+/v40adKE/Px8evfuTU7O6evv7N69m2uuuQaA7t27c+DAAeu+qKgoWrVqha+vLy4uLkRERLBr1y6GDBnCSy+9BEBiYiJBQTXX/hIREREREZFLS1PfilIbdRWZlEvnZj706xDEtuPptXpNSm4RR5Lz+M/ueD7dl8Ct3Zvb7G8b7MnJWtZ4rU5qXjHtgjztXmojPqsQL1cnfD2qhqh/Ft7chwMN/FCxUyVlF9G8mhXJTX1cSc07t+DZMAyScgoJqab90+nZyp/98dl8fyydPu0C+e+eBIZ0bqoVz2dwZ0QLXrj1cuvPof4e1gcMZuZbCPZ2xdnRgfe3n+T5m7vYvPbU4Dk2s4BWAR4M7tyUv/ZrQ/vTvO9S84rs8kHK7pgsIlpX1EX393DhYGIu4aG+HE7Oxf/3h6OaTCY6NvHmWGpeje1kF1gI8nIlI99C+GlWPLcK8ACgf4eg09bCBkgzF+Pu7Fj3Fc9FpdV+sFJp4GXBfH8svc4PLbSXrILKFc/2L9u780Qm/zuYbPd25fTsXuPZ29ubzZs3YzKZWL16NZmZp/80wWw24+X1R0FzR0dHSktLcXJywmw24+39R+0mT09PzOaKycPJyYmnnnqKTZs2sWDBgjP2q7i4GMMwiIyMPMuRXdyKiooa5dgb67ih8Y79fIxb84nurcamsY5d80n9073V+DTWsZ+vcZeXlzea61uam8/xhFzreH9LyGXT8R1c36Hm2r9FpeXEZxZQkhFPqFMxiw+mM6JdzSuBK31+OIfeLdzJKy4jPrcEp9xEIiOTrPvdLLnsjS8iMuTsVugdjM6irY+Jfcl5df79ne7e+iUun1AvU63aDHYq4ocDJwn3vDBWPR+JT6ONexGRkbahWkleHsfiC4iMNJ31+yqvuAxHIC76WJ1fG+juQHpeIY9E+LA92uBy31J+PFr339u5Oh9zSn3NJ8Eupaz9MRKPggCOZxTj7WTwz2v8cHVywCkvkcjIROuxzuXF7D8ajU9xKr9GpdHG34UmZSbu7uDA4dhyfvr1KA45tg/IKykzyCsqIb+4lL37D+LmXPf1lpXX95v9qXRt6kZkZCQuhoXk3CL6tXRlX5xBWUG29foEuZTww6/H8bdUv1I4OiGd69u5cyjVVJaZfwAAIABJREFURHbiCXKSqp93nIvNhAW5EuJawp4jMXR0rblcyPH4NNr5O3M4NoXIyNqHxIlpWeQElRMZWbXtoqIiSI3B1aGcL7b/SvuA2teOtpeEjFz83R357Ug0HgX2fehpUpaZreaCauf9xvr/T6D+x2734HnmzJnExcUxefJkli1bxgsvvHDa4728vMjP/+NTqvLycpycnKrdl5+fbxNEz507lylTpnD33XezceNGPDw8ajyPq2vFG6Zz585nM6yLXmRkZKMc+/+zd9/xbdX3/sdfkixry5Il723HdpxFNoGEkRQCBCgj0FBaoC2X0pbbWwqXS+ngcimj83bR/kpp6S20lFAoUDqYCZAws6czvLdl2ZYtyZY1f38cj8iWbUmW7Yzv8/Hwo7F0dIYsH5fP+Zz350w9bjhzjz3W447nBCvOJ+KzdaY5U49dnE+mn/hsnXnO1GOfifMJgFwuP2Pe34Cxh+cq9w8f71N73uPP+x0UF+RyxaLsiK/Z2+igJL2LRQvmUREI8sDWNzBnF5GZoua1Q208ub2Wp25dGXYLejAY4r/f+ZBbzysnP1VLU3c/8+ZlhK23V9XJ9teOUlFRwYGmHrJM6piGfYWOHGD1PD3v1ldizS2my+2NemjiRJ+tre1VLC3RRvWZOFdu48nttSfN58f5up2zF5RSMao7tFtp552m41RUVMR9Pqls7SUntTOu166u9OHo83HdBUv40Cbn02sX8P1tr1NWPhfFBHEmiTYT55TpOp88klPEVb98j4uWpGJIk5FjHWDl4gURly2o9KE1p1BRUYDz/V5WzCuiYjB/e1Et+NRKKirmhL2m2dGPVd9KikaJ0pI75jMUjaH39/grrdx31TxKMwzk7vOws7mfS5eV8sKhncwtyqWiIh+AFV1qqjtc479fe3ZzzvxMHr0x8rlpyJyyINeu8fHaoTYOtfRgU1hJ0ShZnGcas6zvIxcrSy3SXRwx/Jz8W7qYX1ZMxWAnd6Tj/sSxAK0BLVdUFEe93kTpe7GFskw9eksmFRVTHw55Iqe3ga5+P9mFc8Z0fZ+p//8Epv98kvCoDY1Gw8GDB3n22WdZu3YtpaWlEy6/dOlS3n1XCoXfu3cvZWVlw8+VlJRQX1+Pw+HA6/Wyc+dOlixZwksvvcTjjz8+vD2ZTIZCETmfRhAEQRAEQRAEQTi9WPTJdLlHIhdsbj+blufxw9eORlz+9UNt7KzroiJLyldVKuRcuySHpz6oY8uRdr714gG8gSD/917d8Gs8vgC3/P5jAC4oS6Miy8jFo4rOAHOzjNR0uDja5uTWP+wIW0c0bM4BslLUpOlV3PXcXu7cvDem14+ntsNNcZp+8gWRspM7phhhkUgnDhc8UbpRPeX9bOvxkJkSedDiZG5dU8RX15WikMv4yabF6FRJmLTJJ9V7d7JLN6q555JyXtzThN3lnfAiTaouma7B7ORau3s4igJGhvqNZuv1kG5UMSddP6Wc507XAD19PkoGf4fMumSS5DKW5JuG921IabqB4+3jb8vR58UcReSNUiEnzaAi16yhqbufX26t4pV9LRGXtbsGWJSbQluMURvOft+EURsgRX5Mx7DTyYRCIbrcXorT9MNDJRPF4wvg9QdZUZjKx7M4PPFMlPDC8/33309LSwvvvfcebrebe++9d8LlL774YpKTk7nhhht49NFHue+++3jllVfYvHkzSqWSb3zjG9x6663ccMMNbNy4kYyMDNavX8/hw4f5zGc+w6233so3v/nN4Y4hQRAEQRAEQRAE4fRm1ibT7fYRCkm3mNtcfi5bmEmrw4MvEAxb1uML8JU/7ebhf1ZSkTXSSfyFNUX8+eMG7vnLfh6/aTk/vv4sfv1O9XDBY/txO+4BP8/cdvbwcLtIUjRKbl1TxKef+BBNsoJ3j8c2mKvDOUC6UUW2ScOOui46nAMcbx8/LzZa7c4BMiMM0Isk3aCecnZyovR5/Xh8gbDC3pB0owrbFIf5tfZ4yIryfRltTrqBeaOGw0mDLkXOcyyK0/Q0dPXR6RrAqh/7cx6SqlPR1eelxSENEiyy6IafK7RqI2Y825wDpBtUlKTrp5Tz3NTdT4FFNzyY06xNJsOoxqJLRpesGB4uCFCWMfG2uty+4UzoaOSatRxtc7Krvpvj46y3wznAwpwUWns8w+fBaPREUXi26JPpdM/8+cDtDZCkkJGVosaR4OGCXW4vqbpkVhWn8lFNZ0LXLUws4VEbDQ0NPPzww+zatYt169bxm9/8ZsLl5XI5Dz74YNhjJSUlw/9et24d69atC3teq9Xys5/9LHE7LQiCIAiCIAiCIJwy1EoFSoUM54Afo1pJh9tPkVVHmkFFc3c/hdaRAtXh1l7mZhm448I5LMkfub28wKJjw8Is5menDA8QWzs3ned2NvLF80vYXmXnonkZJCkm79e6dU0xbx2x8dDVC7jh8Q/pdA1giTJuo8M5QLpBTY5Zw7xsI8kKOX/b18Ld68tjfFfCDXV+RsOiS8bp8eH1B0lOSnh/WkxaHB6yTdKdzaMZVEkEQiFcA/6419/W6yEjxsGCE5EKzx6WjPP8qwfb2N3Qzc3nFJBrHj8e9ExSkKqlobOPTrd3wt+Tsgw9L+9tpiLTzuo51uEiMECRVUetvW/Ma2zOAdIMaioyDWze2Rj3Po7uujfrksk2qZHJZFyxKJuCE4rgaQYVvkBw3N97R58XUxQdz0NyzZrhoaPVEQrPoVCITpeXXLMWtVKBo8+HOcKFmkiv6/X4MGomLgVadCo6XYkf7jeZLpdUHDZplRxLwMW3E3W6vFj0ySzISeE379YkdN3CxBL+FyUQCAwPFHS5XMjls/tHSxAEQRAEQRAEQTj9pOql2/ADwRD2Pj+ZKWoKLFrqu8KLUQeaeliYk8JlC7PIHFVwfPiahdx4dv7w97ecU8jTH9YTCIbYXmXnvDlpUe2LJlnBi19ZzfzsFM4utrC9yh7V60KhEB3OAdIMKu69dC73XFLOVYtzeHFP85jO7Vi193rIiLKzVy6XkapLxu6a/a7nOrubXHPkKAyZTCZ1Z0+h67mtJ3KMR7yyUjT862AbT39YP6bz1D3g587Ne9jb6ODv+1vHWcOZx6RVEgpBtc0V1jk82rklVuzOAX7/fh3nlVrDnsswqHEP+HF6wjtjO3o9pBtUnFtiZWddNx5fIK59bHb0k20a+RxWZBo4r1Q6H3z/ukWkGUYKzDKZjJJ0PTURoj8Auvu8MXU8q5UKrHoVN56dT6d7YMyFlt5+PyqlHLVSQVaKmmZHdB33/b4ACrksLMc+Eot+ds4Fne6BwcJzMt0J7ni2u6WLAvOyjFS29sbUJS5MTcKrwnfeeSef/vSnOXjwIJs2beKOO+5I9CYEQRAEQRAEQRCEM1yqTkWn24vN6cGoUqBKUlBg0dIw6vb7A809LIhywNhZeSZSdSq+/+oROl0DY2IVonFheRpvVdqiWrbT7UU9WEDKNmkwqJUsyDFSaNHx7I74uzUH/AFcA35SYyh2pRumnp+cCG8dsY0pMp5ofraRu57bR313fB2ZrT2eMRcgpmJJvolO1wB/2dnIN188QDA4UtA60uakNN3AF1YX8qG4vX+YTCYjL1XLnkYHVsP4Hc8KuYzrluVS2drLmtLwi0ByuYy5WQYONPWEPW4bjK5J0SopzzSwoy6+PN/WHg85JxSelxem8h+fGH+GWeE4mdMeX4BgCLTJsc0lu355LhsWZlFk1Y/peu5weUgb7KwuSddz3DZxd3BPvw+PL0Bvv3SHyGSseuncOtOG4jBMGiU9CS48d7q8WHXJpBlUyGWykyZa6EyQ8MLzypUree2113jzzTf5xz/+werVqxO9CUEQBEEQBEEQBOEMl6pV0u2W8l/TdNKt4/mpOuo7x3Y8L8oxRb3en21azHtVds4rTUMhHxv3MJnLFmSy9agtrEvR0eeNOOjssS1VrJ+fGfaYTCbjG5fN5X9fP8qFP9zKH96vi3kfbL0DpOlVYdEEk0kzqKa9GBMKhfigevwCbCAY4o3DbVwy6j050WM3LmVetpGttfHl97b1eBLa8XzV4hyeuW0Vz9y2ipoON//5l334B7vVj7T1MjfTwMoiC7vquocfF6QBdl1uL1bdxHEwn1qRx7VLcsKKwEPOKbbwwaiCvs05MFyUvaAsjXeOxpa5PqTF0U+WKfrPSaFl7LkHhrqdlRGjYyZy76VzyTZpKI2QVd3h9A4X7OdlGTnSGrnw3NrTz7W/eo9Vj7zFwgde491jHZPmO8NQhr437CLKTOgcLDybtcl0J3i4oBSDkoxMJl2wONzam9D1C+NLeOH52Wef5ZprruGzn/0sGzZsYMOGDYnehCAIgiAIgiAIgnCGS9Wp6HJ7aeruJ0MvFZ5HR230ef3Ud7kpy9RHvd5Cq46X7ljN9zcuimu/LHoVZxdZ+OeBkWiFX79Tw6P/rAxbbn+Tg38eaOXbl1eMWceCnBQeuWYh91wyl19sOR5zprHNGXuOcbpBhc05tcF9kzna7uTTT3w4blTG7oZurHpVWH7uaAq5jPIMA25vfEXcth4PWcbIUR5ToVcl8X+fX0m13c0/Bn/2R1qdVGQZSdUlk2PWcLBFFLuG5FukvGvLBMMFQRq097+bFkd87pwSy5gLGTanh/TBiJkLytJ49VAbvZ7Yu2dbRkVtTKbQqqU2wrDD7hgHC45Wmq4fM2CwoctN7uC+zc0cv4j6ztEO0gwq9j+wns+uKuDVQ21RFZ6Tk+ToVEn09Ce263gy3W4vlsGM5+kZLigV6ysypbgNYWYkvPD81FNP8Ytf/ILNmzcPfwmCIAiCIAiCIAhCIln0yXS6vbQ4PKQPdzxLQ8uG1Nrd5KdqJ800HU2pkKOJ8db4E21cmsNfdzcBEAyGeGVfy5ji0ce1XWxYmIVpnKLUZQuzuHxRFqvnWHlye21M22/rGSDDEEfhuXd6O563HJEiSMbLwP77vhYuW5A16XpSNEpc3tize10DfvzB0KTD1eKlSVZw3bJc3j0mHd+RNmmwJcCq4pEiabSZvKezvNToCs8TWV6QyuHWXtyDF2b8gSANnX3kDWaEL8pN4RNz0/nsbz+KOeu52eGJ2GU9ngKLjvpIhecYBwuOVpqhHzNo71i7i9IM6XNVkWWkcpyO5yqbi8V5ZpQKOcsKzLxfbccYReEZhs6vMxtH0eX2Yh4uPCe249k+OFwQpPdsvC5xIfESXnguLy8nKysLg8Ew/CUIgiAIgiAIgiAIiZSqS6bLPUCzo284aqPAoqWhq294cFSX24tVP/Gt/NNhXUU6R9ucNHX3sbuhG5VSTluPJ6z41eLwjDtE70Rfv6iM379XG1MhRhosGNtxpxnV0xK1cfvTO9nb6ABgS6WNS+ZnsP342MKzxxfg5X0tbFyWM+k6jZokXHF0PLcN5jvHGnsQi/NLrWw73kEwGOJIq5O5mVJO+JJ8EweaHXh8Adb+6O0pDUg8HeSnajGok2K+KHQiTbKCBdkp/OSNY9R3utnf3EO2SYNl8HdeJpPxwCfn4/UHOdIWfaHRGwjR2+8bjuyIRqFFS729L2xoXZXNNRwfEa9FuSb2NTrC1nus3UlZhnQXR1aKGq8/EDGfvarDxZx0abllBWY8vmBUHc8AFl0yna6ZzXnuHOx41quS8AaCcQ+GjLzuAayDhecFOSnsa3IkbN3CxBJeeF61ahUXXXQRN998MzfddBM333xzojchCIIgCIIgCIIgnOFStcl0uX3UdLjJMkjFFINaiSZZMVyE6Zpi0SdeqiQFly/K4sXdzTzzUQNXL86h0KILy3mO9lb+QquOSxdk8vi7NVFvv/2EuIFopRtUdCQ4asPjC/BWpY0/fVhPt9vL0TYn91xSzrvH7YRCIbz+4HD37z8PtHJWrolcs3bS9RrVyriiNtp6PGTG+L7EqsCiQ61U8PcDrWhViuHP3/xsI4dbejnS5sTrD3K0/czuuCzPMLCyMHXK6/n2FRX09Pu4/eldbDtmZ82c8MGUMpmM4jQdDV1j85fHY3f7STfGlpFu0iajUMjChvJ98emd/G5bzbh3NUQj26RBqZCH7f/xdhdlgx3PMplM6uBtGxsdUWUbKTxnpWjITlHHUHie+QGDQ3EYMpmMAouO2gjDGuPV6fJiGYzaKMvQ4/EFqImQuy8kXsILz5s3b+anP/0pDzzwAP/zP//DAw88kOhNCIIgCIIgCIIgCGe4VF0ydtcAB5p6KLeOdCbmp47kPM9W4Rng2qW5PP5uDXsaHXxudSFzMvQcbz+h8NwTfYbsly4o4bkdjVFv29Y7QEYchedEdzwfbO4hy6TmtUNtfO9fR/hERTpz0g3oVAqOtbvYetTG15/dC8CLe5rZtCIvqvVKURuxF55be/oTOlhwPGtKrdz93F6+fEHJ8GNFVj3tvQN8NDgM72gMHbino8wUNb/73Iopr2dRrokfXCflsf/+/VrWlFrHLJOfqqMxysJzn9fPUftATPnOQ0bHbTj6fOxr6sE8hagNkLrl9zRIHbo9/T56Pb6wGJD52SnsrOsOe02/V+qCzjvhroolBWaM6uhiZiz6ZDpdMx+1MXS+jpRtPRVDwwVBKtavLU8fjv4RplfCC88ZGRksXLiQ4uLi4S9BEARBEARBEARBSKRUfbI0jM6gwqgeuV2/wKKlfjDnudvtndJgr6lYkmfiExXp/PqzyzCqlYOFlJFio9TxHF0RNM+sxTngpz/KXON4ojbSjeqEZzzvbXRwYVk6KwpT2d3QzYNXLwCgNN1Ard1Fc3f/8EC2ylYnS/PNUa3XGGfheShqY7rdsXYOL9+xhs+tLhp+TCGXUZZp4C+7mpibaQi7CCFMjUwm44vnF9M3EODsIsuY5/NTtRHzlyN58JXD/GZHJ6uKx65nMoUWLbV26dwTDIbo6fexsih1ynE/S/PN7Krv5nfba9l2vIM56fqwbuwbVubxp4/q6fOODCGt7nBRaNGRpBgp+335ghKuPCs7qm1a9Crs0xy1UWd388DfDg1/P7rwXJWguwJ6+nz09PvCfg5r56az9agoPM+EhBeevV4vV111FXfddRd33303d999d6I3IQiCIAiCIAiCIJzhLLpknB4/i/NMYY8XpGppGCwydfV5pzS8bCpkMhk/u2EJ5ZnSLfFlGQaOtjnZUddFvzdAr8ePVRddQUoul5FpVNPaM/lQulAoRJ3dHXPHZoZBRVeflwF/4nJV9zQ6WJxn4pFrF/LnL67CqJY6P3PNGpq6+2lx9NPhHMDW66HP64+6WB5v1EZrr2dGOp5zTBrmZRvHPD4vy0iVzcXGpblnfNRGon3yrGz+dNvZEYeCDmW/D/gD/OzN42z42TZePdgWcT01djffOD+duy4ui3kfFmSnsG8wz9zl9aNRKvjtLcv59Mr8mNd1oiX5Jp7d0cCv36nm7uf2UZoePkutLMPAyqJUnvmoYfix6hPynYf3LydleCjhZKxTGC444A9EjP4YbU9jN8/vaiIYHMnkHy48ZxgS1vH81pF2zp1jRa0c+WysmWNlb4Mj6ot5QvwSXni+/fbb+c53vsMNN9zApk2b2LRpU6I3IQiCIAiCIAiCIJzhzIMFiiX54YXnfIsuLGpjtjqeRyvLMPBmpY0bn/iQl/Y2k5WijilDNitFTWvP5BnM+5t6UCkVFFt1Me1fkkJOrllDQ2f0WbhDOpwDXPur9/D6pWLwO8c6WP+Td/iwupPF+SYyjOqwbsMck4ZmRz8tg4X0t491UGDRRT30T62UEwyFYh4+1tbjiTmCJJHmZRtRyGV8cnE2x9udwwU3YeqSFHJWjJMZnZ+qpbGrn6c/qOe9KjtfXTeH+/66P2LGb2NXH5mG6OIoRju7OJWPaqUolZ4+HykaJUa1MqzgGY8FOSlcvzyP1+88n2uW5LCqeOxx3rSqkL/vbx3+vrrDTUlabOeAE1l0qriHC76yr5X/en7/pMvVdrhxDfipsbsZ8AcY8AeGo0BKM/QcS9DFmVcPtnHp/Mywx3SqJPItOqoSGOchRBbfb9MEVq5cmehVCoIgCIIgCIIgCEIYgyoJpULGkjwzOEc6gQstWv744Ujh2TJLGc+jzUnXs/3etWze0cgzHzWQnRJbR3K2SUOLY/KO51f2tXDFoqyoi7gnKrbqqO5wR90VOeTFPU3sbnDwfrWdC8vTeX5XE/OyjDj6fRRZxha/cswadtR10e4cwKJL5u2jNoqskw8VHCKTydAnK+j1+GIq6tXZ3RTGWJBPpCV5JhZkG8kwqjGolTQ7+slLjf64hfhkpajpcA7wyr4W7ry4jLXl6bywu4lj7U6K00a6ggf8ATpdXqza+Epl87KMtDo8dLoG6On3RT3IbzJqpYJHrlkIwPc2Loq4zPwc4/DFDLlcRqdrgLmZsf0enygzRR3TQMYTbT1iwx5FXnyN3Y0qSc6BZgd6VRJmbfLweavIqqOxux+vP0hyUvw9s31ePx9Udw7ngJ9oKP5oYW5K3OsXJpfwjmdBEARBEARBEARBmG4ymYzvb1xERVZ4cSXfMpLn2uX2DndGnwxyzVouLE/jQHNPzFEY0XQ8B4Mh/nGglSsWRZfjOlqRVUetPbos3CGhUIi/7GzioooMXjvUTr83wNtHbXzninn83+dXRuzqHup4bnX0s6rEwrbjdopiLAjrkuX09vsnX3DQgD9Ak6OfwgiF8JmyICeFv35lNQDlmQYONvfM2r6cSZIUcrJMaqpsLs4tkbKbi9P01NjdVNlcvHpQ6hRu7u4nM0WNIoY7EUZvZ1mhmY9ru3D0+TBNcahgLIxqJSZtMo3dUrG4p9+HcQqF7/nZRmo63LgHov8dA/AFgmw73oHd5SUUmrijv9bu5qKKDPY19tDpHggbBKtKUpBr1lAXZTb3eA639FKcpsMU4c6XRA8wFCIThWdBEARBEARBEAThlHTt0tyw4VkAaXoVA/4gvR4fXW7fSdPxPGRxnhmTVklOlIMFh2SZNJNmPB9u7UWtVAznSseqyKqn1h5bIWZ/Uw/eQJBvX17BG4fbeHFPM4tyU7BMMFBtKNKju8/LigIzTo8/5oKwPllOr8cX9fK1djd5Zs2UuicTYaioeVFFOv8cJ2dYSLz8VC0XlqejSpI65IutOmo63Lyyr4U/f9wIQENXH/lT7EBfVWzhw5pOevpntvAMMDfTQGWrFE8x1cKzWqlgfraRvYOZ1dHaUddFoVWHSjn+haE+r59QKESt3c0nF2dzoLknLN95SJFFR12MF8JGszkHxh0oWpqhF0M+Z0DCozZuuummsFt6lEolmZmZfPnLXyY3NzfRmxMEQRAEQRAEQRCEYTKZjPxULfX2Phx93oidbrNJIZdxcUUGJaMGf00mO0XNW5XtEy7zfrWd1XMsce9bcZqOF/c0caStF4NaSc5gV7YvEOSfB1qZn21kzqjBZm8cbmfDwiwKrTqKrXp+u72G71wxb8LtpOqS8QWDpBvUw1EHxTHm0eqT5fT0R194PtbuoizGCJHptGFhFj949Sh9Xj/a5ISXZoRRLqrICPuMFVl1PLezEdvgcEuAxu6pR5/Mzzby7rEOyjINCYvaiNbcLANH2nq5dEEmvQmI+lhemMqOui5Wz7FG/ZoPqjs5vzQNl8dPh8tDSoTi+2d/+xFry9PRJis4t8TC1zfvpb13YEzh2apXYY8zZ3pIh3OANEPki2Bz0g1U2cSQz+mW8Et9ubm5XHnllTzwwANcffXVaLVaFi9ezLe+9a1Eb0oQBEEQBEEQBEEQxii06Njd0I0mWTHrHa6R/OC6RVy1OCem12SlaGh1TBy18X51J6tLoi8SjVZslYZtffGpXfxyaxUA1R0uLv3puzz0j0r++GHDmNdsOWJjbXk6AM996Ry23H3h8Pfjkclk5Jg05Jg0w53OsXY8S1Eb0Reeq9qdlMZY7J9OFr2KJQVm3qy0zfaunBFuObeQ80rThr8fitrY1+igfajw3NVHXmpsETijDcXIOPp8pGhm9qJXeaaRI4Mdz70e/9QLzwVmdtV3T7pcKBTil1urCIVCVNlclGUasOpVdDjHFo1dA372N/Xw2NYqCi06DGolc9L1vHm4fczdKWkGFXbX5FnRE7E5PaQbInc8F1i0tPZ4Yh5SKsQm4X+BW1pauP766ykuLubaa6/F5XJx/fXXEwiIH6QgCIIgCIIgCIIw/c4psfDKvpYxHXQxcdvh2c9AT3PidmxQPIP/sk1qWiaI2vAFguys62ZVcfwdz2kGFV5/EJ0qibcq22nv9bDp8Q/44vnF/PLGpewZddt9W4+HZkc/S/NNMW8rx6wly6Qm26TmtvOKYv5ZSVEb0efPHre5mHMSdTwDXFiWxo7artnejTOSVZ9MIBhCr0rCPRBgwB+gMQFRG9km6QKRdLfFzHY8V2RKHc9AQoYbLisws7fBQb93bD3PFwgO/7vW7uaHrx2lqbuf6g4XJWm6iEVjjy/AzroulhWYKUnTD+e6X1CWxpYjtjF5/FZ98pQLzxN1PCsVcgosWmo6phbnIUws4YVnn8/Htm3bcLlcvPvuu/j9fhobG+nvn3z6riAIgiAIgiAIgnCKcNuhbjv0x5YBOhMuLE9jZ3035qnEbDTvgoYP4HcXS8c6y1I0SvyBEK5Rw762He+gy+3lvSo7+anaKQ1TlMlkXL88j59uWoxelcTXnt3DJ8/KYdOKfBbmpHCszRnWHfjOMRvnlVrH5GxHI8ekIdukIUkh51uXz4u5GB9Lx7N7wM+xk6zjGSDdqKLTPbXCmhAfmUxGsVXHknwTaQYVtt4BGrr6yDNPrfCsViowapQca3fNeNRGoVVHU3c//kAwIVEbZl0yq0osPL+7acxztzz5Mc9+LN0BMZQDvb+ph/rOPoqsOqz6ZDqcI5/trUdsfOLH7/D20Q7OLrbw0DULuGFlHiCdr72B4JiOZ6tBFbaOeNicA6SPU3gGqMgysq/p5Pt3isTBAAAgAElEQVQbdjpJeOH5e9/7Hps3b+b666/nhRde4JFHHmHv3r3cd999id6UIAiCIAiCIAiCMFu2/Rhe+Df448bZ3pMxCiw6iqy6qQ0WtFXCok1Quh4+/FXidi5OMpmMQquOwy29w4/5AiG+9PQu1v/kHe56bh9fu6h0ytt54JPzKc80cNG8DHY3OLj9gmIANMkKStJ1HDph+x/VxJb/eqKbzyngumXxz4HSJyuiKjy/ebidJQ++gd3lHe6wPFlY9SrsEeIIhJlRlmFgeYGZDKOKFkc/NR3umLPGI8kxazjc2otphgvPSoUckzaZhq4+kpPkKOO4IDTabecV87ttNQSCobDHD7f28sPXjtLT52NvowOTVskbh9uw6lVok5MG85lHisav7G/B4wvwhw/qOLsolaX5ZpYVpAJwVq4JozqJVF14gXj0OuIxUcczSN3WW4+c3HE3A/4APX3RxwqdbBKeYJ+fn89jjz0W9lheXl6iNyMIgiAIgiAIgiDMprptcM2v4ZlNEPCBYmaLLJO5oCxtTHdwTDqOQP45UHQ+PLEOVn8N1CmJ28E4XL04m+d2NrKySCrYHLV7KErT8b+fkjqUs01Ty6c90Y0r8ylNN5BhHMlHXZJnZk9DN8sKzAAcbOnhC2uK4lp/RZZxSvunT5Zj90xejPn9+7X86FNnceWirLgiTqaTVZ+MXXQ8z5r/uWo+SoWcj2q72FHXhUWfjEE99fNYrknDvkZHxMF60y3doOJYuwtjAo4DYEWhGYtexZPba7ntfOkiVLfbSyAYYsNZWfzkzWPsaXBw3dJc/vxxA0sHzw1pBhW7G6R8aF8gyFuVNp76wkoeeOUQS0ZF8yQp5Nx1cRmLcsPPr4kYLih1PEfOeAbp78R/v3yIO5ZOrdN9Ov367RqqO1z8/NNLZntX4pLwjudf//rXLF++nDVr1gx/CYIgCIIgCIIgCKeR/m7oqoOC1WDIgq6a2d6jMW47v5hb4yyKAlLHc3oFpBZByVrY92zidm60xh0QDE662MZlubx2qI2ewU7fva39rC6xUpZhSGjRGaSu8dEdyUvyTcM5z/3eAA1dfZRmzE58hRS1MfGFhSqbi6NtLi6dn3nSFZ1hqONZFJ5nizY5CaVCToZRzTvHOpibObWLIUNyzNLvommGhwsCZBhVVNmcCYv5kMlk/PzTS/jt9hq2H5cih2rsboqtOu5ZX84r+1o4bnNy/fI83N4AJWnS+UAaLih9tj+s6aTQquOsPBMvfmU12uSxPbCfW11E3qh87bQp/n4EgiG63V4s+vF/Dha9ijkZeg62n5zxwKFQiBd2N3G4VbrT5P6XD9IbxQW3k0nCC8//+te/2LZtG9u3bx/+EgRBEARBEARBEE4j9R9A7nKpyzl9HtgOj12m6i1wd878vg3KMWni76oNBsF+DNLKpe8XfgoOvZS4nTtRwAdPXQVt+yZd1KpXsWaOldcPtQGwr9XDuXFGXcSjLMNAtc0FQGVbL3PS9aiSFDO2/RMZVHK6+ybuhvzngVauXpxNclLCSx8JYVQr6fcF8Ponv+ggTJ/MFDW7GxzMzUzM8MmcwYtAs9PxrE54vnSOScOXLijhnwdbAajpcFFk1WHWJfP1i8uYl2WkNF2PXpVEyWBUiTRcUPr9fK+qk7XlaTFv16hJYsAfDMuVj0Wne4AUjXLSyJH18zJ5ak83NR2uuLYTjx+9dpQPqif/+7ijrpskhYzGrj5svR6e+qCeQ829bD1i43/fODYDezp1CT/75uTkoFaP38YuCIIgCIIgCIIgnOLqtkPhaunfGfOgfVTh2dcPz3wKfrEEmnbO/P5NlaMeNOaRaI2StVJx3dEAtiOJ3VbLXvC5wV4V1eILclKo6nDR7w1Q1TXAikJzYvdnAgUWLfWdfYRCIQ4197Age/aiR3KMSqonKRS19ngoOMlynU8kl8tI1SXT5RY5z7Mpw6giEAwxNyuxheeZzngG6ViO21wYE7ztsgwDVYMXnWrtboqsUmfzZ1cV8Mxtq5DLZZxTYmFBjnROOHEwYHWHi/KM2N9bmUyGRZ8cd87zZPnOQ247r4iz87T8+zN74tpOrEKhEH/Z1TjcxTyRv+1r5vpleRRYtPxllzTk8bjNyfYqO68OXgg42SW88Ozz+bjyyiu56667uOuuu7j77rsTvQlBEARBEARBEARhNlW9CUUXSv9Orxjb8dxVA6klsOLf4PjrM757w4IBaDsQ22v+9Q144daRbmeAJBWUXwaPXwC/vUjqUk6Uum0gT4LO41Etnp+qpaGzj+M2J9kGZcTb1qeLQa1Ep1Jgcw5wsLmX+TmzV3hO1yXRNxCYsGhrdw2QNsFt9icDi27qA9SEqRnKMU9U1EZuqgalQoY2eebvBkgzqqnuSGzHM8CcdP3w3Q619vAhjGqldJxP3LycJfnShTCrPplO9wDBYIiaDhfFafFF8pzYOR0rW5SF5ySFnMvKjLT0zEzcRnWHm/beAWxOz6TL7mvsYWVRKuWZRp7d0YBJq+R4u4vDLb0ct7lwngKxGwn/C3XbbbclepWCIAiCIAiCIAjCycJWCV4X5CyTvk+fD7aHw5fprALLHMg7Gz745czv45DqLfDczfCfx0E1WPjorIbDL8N5d4Uv6x8ATy/sewbOuxvMo/Khz/l3yF0Bu34vdXEXnJOYfax/D8o3gD26wvNQ13GVzUVeysx3VBZYdNTa3exrcnDDyrwZ3/4QmUzG3CwDR1p7x40bsbsGsOonLzzNJqtBFJ5nW6ZRTXKSnEJLYgbMFaRK+eizkSueYVDh9QcTXnhON6jwBoJ0ub2DHc8T30mgSlKQa9ZyuLWXxu5+CuJ8b6eSg94xyWDBExlVclweP15/cNqjeT6otqNRKoY7wsfjDwQ5bnMyN9PA3EwDr+xr4XPnFlLZ2svRdicFqVr2N/WwegbjluKRsHdz69atANTU1FBbWxv2JQiCIAiCIAiCIJwmDr0E864G+eB/TlpKoLdZitcY0lklPZ67App3Q2DiIXDTpnor+D1Q+Yr0fXcd/OFK2PZj6BjMxwwG4LVvwQ9L4bVvQsWVsPprMO+T4evKXAArboWSdVCzNTH7F/BD48ew9OaoO54LUnU0dA0Vnme+m7fQomNPg4Om7v7h2+pny9xMI5VtznGfPyUKz7rkuDs6hcQotOj4v8+tIGmSLOBoaZIVPHrtooSsK1bpg93biS48y2Qy5qTrqWztpa7TTWEUETZL8828uKeZrBT1cFd0rKz6ZDrivDCzv8kR1pk9EflgrEene/ovAr1X1cn6+RmTFp7rOt1kGNXoVEnDUSWfWp7HnkYHSXI5F1VksHdw2OvJLGGFZ4dDOli73U5HR0fYlyAIgiAIgiAIgnCaOPwSzL965HuFElJyobt+5DH7YMezNhWMWZGHD86Emq2w6iuw78/S9weel7qLV35R6lwGeON+KWd5ww+lY1v+hYnXWbxWKmgnwge/gOzFkLdS6sQOTj5kLkWrRCGXsbOumzzTzHc8F1m1bN7RwPJC86RDu6bbUMfzeOxOL9YobrWfTVaDik7R8Tyr5HLZjA7pnE4ZRunzbtQkPoJnTpqen715nOUFqehVk69/WYFUeC6JM2YDpI7nyQq0kXh8Af6+v5VrluRE/Zo0Q3zbOtE/D7Ryy5Mf88cP6yM+3+X28mFtJ1cvyZl0W5WtzuGBl4vyUrioIoOKLAOqJDnzso0szjfxYU0nR9omz4qeTQn7K7F48WJqa2u5/PLLx3wJgiAIgiAIgiAIp4H+buhpgpzl4Y+bi6Rc5yGdVWAtlf6ddzY0fjRz+zjE2S51Yq/9plRYdndCdy1kzIdlt0jF6PcfgwN/gU89BWdtkiI5hiJExpN/jlRI90zxP/Zb90vbv+qX0hDDZD04W6J6aYFFy66G7lnpeC6w6Kjr7OPcEsuMb3u0iiwjleMUXdwDfkKE0M1Czm4sLDppeFooFOJwSy8eX2C2d0k4hVn1KmSyxHc8A5Rm6Pm4rovPry6MavnlhWa63F5Kouw6juTcEivP7WzEPRD9XTN7Gx388cN6FueZyB4c9BiNeIvcJ/rNuzUsLzDzo9eP4ujz8tKeZvyBkQuKP33zGFedlc2C7BRskxaee6nIknLH0w1qfnvLcmQyGaXpeiqyDKwoTKWu083Vv3yPwy0nb/E5YZdA7r///oiPy2QynnrqqURtRhAEQRAEQRAEQZgttiPS0D35qB6m1GKpqLv3z2A/NpLxDJB1VuwD/hKh5m0oPA+SdSMDELtqYeH1YC6EDT+Sis9X/hx0g0VUjWny9SrVUod3bzOopzCM7Mg/YMlnwZQvfW8tlXKeU3InfWleqpaDzT3kGGdusOCQoWzXc0tmv0O0PMPA8XYXgWAIhTw8T3coZmM2cnZjYdGr2FnfzTW/ep/Drb187ROl3LF2zmzv1szrbQFvH1jPwGNPIKVCjkWXPC2F53lZKRSn6Vhbnh7V8nPS9BjUSVPqeF5TauXsIgs/fO0oD3xy/qTLe3wBPvX4B6Rqk3nk2gUxbStNH13eeigU4rwfbOXlO1bT6/HT5fayrMBMMBjieLuTP3x+JXWdfVz7/96npsNNVoqas4sttPb087d9LWy5+0JSNEp6+334AkE8vgAG9dif15E2J59aPjZH/9IFmSzISSHDqGbbf63jv57fx676LuZlJ2Y4ZqIl7K/U008/nahVCYIgCIIgCIIgCCejjkpIqxj7eGqRFBXh/kjKU07Wgy5t5LmhjOWZdPw1KL1Y+ndaOXQckQrPqcXSYwuvk77iobWCuwOI8F5Eq3UvLL5x5PuM+dC8C0rWTvrSglQtealakmch6qLIqmNlYepwJ95s0qmSMKiT6HAOkJkSPkTM7vKe9PnOIGXYvnG4nRtW5PHgVfP58h9386ULSsYU0kEqeN385Mfcu8owC3s6DQJ+eGy5dFdE9VvSBaJbZuFccZrJNmlI1SX+bog1pVb+8dXzkEf4bEYil8u48ex8lheap7Td+zbMZe2P3uY/LymfNOKjyuai2Krj1TvPj3k70UZttPcO0NTdz9F2Jx9Ud9LY1ceyAjPNjn4MaiUpWiV3rC3hu38/zNlFFt6rsnN2sYUddd2cXZQ6/LMx65LZftzOA68c4p17Rs77tl4Pl/9iO44+Lw9eNbbY/sXzS8K+X5pv5uPaLm5K0LzbREvYX6k1a9aM+yUIgiAIgiAIgiCcBmxHIH3u2MdTi6WojeY9sP5hKL4AhjpNzUVSN/RMCvig6i0ovUT6Pm0utO6Dvk4wRp/5OS7dUOF5Clr2Qtbike/nXQUH/xrVS4usOkrTZ6f4qFMl8dyXzolYGJ0NOSYNzY7+MY+fCoMFAUrS9JxdlMr9V85jUa6JNIOKtyrbIy5bY3dT0+E+ad77Kat9B1QGaRDpFT+Rzh+entneq1Pek59bweK8KO7eiIMmxuia+y6rYM4Uz1VWvYqVham8fqgNkC7ABIKhiMtWtvYO5yLHs51oCs9H26WBptUdbo61O2nvlV5ztM1J+eC2i9P0/P7zK9mwMJP3qjsB2Nvg4KwTfi7pBhWv7G+hoasvLGKnvquP7BQ1u79zMblm7aT7s7TAzO6G7ugPdIYlrON5+/btiVqVIAiCIAiCIAiCcDLqqITS9WMfNxdByx4IBuDsL8E5Xxl5zpQPzjbweyEpji48Xz/UvAPll0a3/LYfgzFXitMwZkmPpZXDez8FUx7IE5D5q0sDtz3+1/e2QsA7ErMBkH+uVHRrPyR1P0/gyrOyubA8HXtTzYTLnQlyzFLheVlBeFel3TVAmmHmM7BjlZeqZfPtI62KN67M58U9zayfnzlm2V113WOO85R26K+waBOc++/S97v+ANVbYP41s7tfsykYkIaglq0HTXw/61Phgkusrl6Sw3M7G7nyrGxuf3oXC3JSuOvisjHLHW1zMjfOuzHSDCp21ndNutyxNieqJDnVNhfH2l3D11iPto8UnocsL0ilsrUX14CffU0O7l4/ss9pBhVvHG4nFIKm7r7hAr2tV7qDI1L8RiRz0vR0ur10ugawRPGz7/P6cfT5Ysq/noqE35dz9OhRNm7cyJo1a7j66qs5fHiWphcLgiAIgiAIgiAIiTVex7O5QBo8mL14bP6zQgmGTOhpjG+bzbvhpS9DKHKHW5j2Q/Duj+HF26H8spHH0+aCq10qkCeCLm1qHc+te6X36sT8YbkcFm6Uhh1OQq1UkGY4/YpL8cgxaWjujtDx7Dw1ojZGWzs3ne1Vdrz+4JjndtZ3TTm24KTh90o55/OvHnms7BI49vrs7dPJ4OAL8Pq34edLpMx3AYCL52VQbXNx3ve3squ+m9YIdzmAlIs8uvgbrTSDCrvTO+lyR9udXFCWxqGWHpq6+7ANdjwfaXNSnhG+bU2ygrNyTbx5uJ3K1l4W5YZ3PDs9foqtOuo7+4Yftzk9pBvCo4MmIpfLWJxnYneDI6rl/7a3hXtf2B/1+qcq4YXnhx56iIcffpjt27fz6KOP8uCDDyZ6E4IgCIIgCIIgCMJ0at0Hf9wY/lhfF/g9kaMqklTSULycpZHXN5W4jd5m6O+KrnB94HlYcSvc9CIsv3XkcWM2JBtG8p2naqpRG6NjNobknwPtonkrFlLURt+Yx0+VqI3R0gwqStL07KjrGnPb/67606jjuWYrWMvDh2mWXwbHXpUuYp2JAn54+1HY+FtY/BnY/9xs79FJQ61U8O5/reW3tyznwavm4+j3RVzuSFsvFZnxdzx3RDFc8Hi7kw0Ls9hV302RVYc/GMQ14OfYOEXvr66bw7dePECuWROWUZ1mUJFmULGm1ErdCYXnDucA6TFeWFxZmMrHtZ1RLdvY3cfeRgfBceJKEi3hhedQKMTcudIV8IqKCpKSJk7zCAaD3H///WzatImbbrqJ+vr6sOe3bNnCxo0b2bRpE889J/3S+Xw+7rnnHm688Uauu+463nrrrUQfhiAIgiAIgiAIwpmr7SDUvA0DLun73lb421chd3l4l+6J0uZKQ8IiSS2SBvvFo7dZ+t+WvRMvFwpJheeF10sD+vRpI8/JZFLcRmoiO56nELVhOxw5TkOfAc7W+Nd7Bso2aWhxeMY8fqoWngE+MTddGkz2yJt87dk9OD0+7K4BbL0DzI2zqHbSOfhXWHBt+GMpuTD3cnj/F7OzT7Pt+GugS5cy8udfA4dfiu5OjzNEkkLOgpwU0g1qeiIUnjucA/iDITKM8f3eR5PxHAyGOG5zsbY8HaVCTlmGgQyjmubufuo63cxJ1495zblzrHx+dRFr5qSFPZ5pVLOi0EyBRUdDp5u3j9po6OzD5hwgPcZjOHeOhferoys8N3X34/T4qepwxbSNeCW88JyUlMTWrVtxOp1s2bKF5OSJM5XefPNNvF4vmzdv5u677+Z73/ve8HM+n49HH32UJ598kqeffprNmzfT0dHB3/72N0wmE8888wxPPPEE3/3udxN9GIIgCIIgCIIgCGeurhoI+qFph3RL/J9vkDKTN/1p/Nds+iOUjZPDbC6C7rr49qWnGbQWKZ5iIrbDUn5z5sLIz6+8DUrWxbcPo001asNRHzn2w5AlRYLMhM7qmdnONMsxj43a6On3sbfRQYFl8sFcJ6PLFmahVyXx6p3no1TIueXJj7nz2b1cvzzv9Bgs6PPAsX9JAzVHu+Be+Pi38NgKOPzyzO/bbGr8eOQclbMMvH1gq5zdfToJmbRKevrCC8+hUIgfv36UdeXpyMa7ODoJozoJXyA4Zt0nauruJ0WjJEWrpDhNP1x4/rCmkxyzBrUy8gyB/7yknO9cURH22HXL8njo6oUUWrTUdvbxrRcP8q+DrVLhOYaoDYBFuSbqO/vodk8eFdLc3U+OScPu+pm5syDhheeHH36YF198kU9/+tO8/PLLkxaFd+3axXnnnQfA4sWLOXjw4PBz1dXV5Ofnk5KSQnJyMsuWLWPnzp1ceumlfO1rXxteTqFIwHAIQRAEQRAEQRAEQdJdCyn5UP8+bPmuVBBd/xAkT1DIU6rH74Y2F8ZfeO5thrLLpOGFE+k4ApkLxt+Hs26Qup4TYajwPJTHGqvueikXO9J6+zqlW+6nU08zPLb8tChq5Zq0NDv6CZ3QGXrv8/u5ZH4mC3JSZnHP4jcnXc/zXz6XsgwDP9i4iAU5KaRolHzr8orJX3wqqN4CmYuk7PfRTHlw+9uw7tvw6n3ScNEzpeu3ZQ9kL5H+LZNJ+dcHRNzGaCatEkd/eIH1Hwda2d3QzYNXL4h7vTKZjKsWZ/OTN4+Nu0xtp5uSNKmr+eKKdFYVW8gwqnn3WMeYfOdI6z+RJllBqi6ZAouWD6rtNDv6OW5zYev1xJzhr1TIWV5o5qMo4jaaHf1ccVYWuxtO0cLzU089xc9//nP+/ve/87Of/YycnAj5XydwuVzo9SOt6AqFAr/fP/ycwTDyg9PpdLhcLnQ6HXq9HpfLxX/8x39w5513JvowBEEQBEEQBEEQzlxdtbD4RtjztBRfcdUvxy/oRmOqURtzN0hRGxMVoOxVYC2Lbxux0lmlqI2at6Hho9he6+mBgE/q4h5NkSQ97rYlZDfH1dsMoSBseWh6tzMDjBop3rO3X6ojVLb2sq/JwX0bIgzBPAXJ5TIevGoBv/zM0tOj2xmgeScUnjf+86nFUjd0zlJ4bKV0kWQ2dRwF5zTfiRAMSue4E3Pyl30O9vwR/JPnDp9JTJpkHKO6knfVd3P9srywDOV43HdZBf840Mr+psiD+uo73cN3Uty1vpyVRalkGFR8UNMZ91DDXLMWfzDE+nkZHG93YnfFnvEMcE6xhQ8midvw+oPYXQNcPphRzbOfQe6d3siNqf1EIqiurqa3txejMbrcIb1ej9vtHv4+GAwO50KPfs7tdg8XoltbW7njjju48cYbufLKKyfdzsDAAKFQiMrKU/+Kbjw8Hs8Zeexn6nHDmXvsM3Hc4nwiPltnmjP12MX5ZPqJz9aZ50w99pk67mAwmLDtlNmrqF3yTYr7fkrjeT+kr8EGxF8MlXu9lHbVcPTw4ZgL2KVdDdT2GSlEQf3ON/Hpc8Oe19p241dl0VOzE3fmKnpm4jMWCjHX14f3+Nskebo5FsNxqbqPka3NpPbIkYjPFypNtB34EE/q5N2t8X62DI07MGWdg7rufeo+fh2fIS/mdcy2E4/dqpGxbc8hSlJV/G5nJ+flqak5Pn7X4qluJs4piTyfjJZb/TE9RRtwTrJ+RemXUKdXkf3Bd6jZvZ2AJsLFmmkw+v3Nef9b9Jvn0lVx07RtU+lsIF+hobqhAxiJ8cnTF9Lz5v+jt+CSadv2kFPlb3QoFCIQDLL3wCFUSVI/bWWDjZwSA5WV8RXpTzz2G+Yb+O8XdvPI+qwxy+0+ZseqSwp/nzw99HkD6Pw9cb9/5+br2Fiq5Gv/sOELhLA11tAZ44UmjbeP/XUT70Or04dZrUDe04Kvtx1//9v0lX19Wn/u01J4XrVqFWazebiNfPv27eMuv3TpUrZu3cqGDRvYu3cvZWUjV6hLSkqor6/H4XCg1WrZuXMnt956K3a7nS984Qvcf//9nHPOOVHtl0olXS2oqDhNbk2JUWVl5Rl57GfqccOZe+yxHnc8J1hxPhGfrTPNmXrs4nwy/cRn68xzph77TJxPAORyeWLe3/5uIMic5RfBomoKVGOHJcXlVQ0V+VbQp0f/Gp8HfC5KF6+G6jXMUdohex4c+IsUVTH3SvjnNdgKryLFZyNl0QVk587QZ0yXjqqvDZJiPK7KKsgsH/9ntbuQIosa5k5+HHH/Tjm2Qt4i0KiYk6qAOafe7+WJxz53Zx9BfTrl5Vlsf3ELT926krJJbns/lc3EOSVh55NIXmvAsGQ9WOdEsfAaaHqZMp0TytdMz/6MMub9fbsdo7qEjOn8+7X/IBSsHPuey76O/u1HyVn/VSnDfhqdSn+jzboWsgpKyDBKWciO1zo4Z1EpFdnxxeuceOxzyoK8/ON3cCSnc05J+MUO50c7uKwij4qKkZiYhQPNsLOLTyyroDgtvr+XfxrcdsrWDoIhWDB/XszrUFpcPLl3x4Q/w+5qO4XpbhbMn8fN+Vux9S1ErdFN6/kk4YXnrVu3xrT8xRdfzHvvvccNN9xAKBTikUce4ZVXXqGvr49NmzbxjW98g1tvvZVQKMTGjRvJyMjgoYceore3l1/96lf86le/AuCJJ55ArY4tfFsQBEEQBEEQBEEYpatWisaQySBRRWcYiduIpfDsbJHypeVyyD8HGj6Aum3Q1wUfPCatz9OLru0j6KkCSzSFrATRWcGYBfIksB+b/Li8ffD+L6ScbFOEfOchhkxwtSV2X0dztkj7PtALzmne1lQ526Rhl5mLxv08zss2cqilh0yjGpNWeVoXnU9KA67ozxUDLnDZpPNBtLKXQvNuKL8svv2bimAAOqvAlJ/4dQf80nlszidg1++hfMPYZco3SOeNPU9L0RsCACaNEkefjwyjmlAoRENXH3mpiRkmqlTIue38YjbvaBhTeK7rdFNo0YU9lmFUo0qSUzDq8XiUphvoimJAYCS5Zg0tPR4CwdC4sTxN3f3kmjQAXKip5aPeUqY7lCjhhedYyeVyHnzwwbDHSkpKhv+9bt061q0Lnzz87W9/m29/O44BDoIgCIIgCKew3Q3d6JKTKMvQxz2xWxAEYVLdtWCOoSgULXORNGAw/+zoX9PbAimDc4Pyz4EPfyV1ZH/lI6h8Bf51D6x/GM2b/wOaFNCYEr/f49GlgaVEGn5mPwaFk3RjdlXD249A7gpYsHH85QyZ018M7m2RCrmeHnC2Tu+2pur9X8D+58CYDV98O2KkyfxsI//3fh3qJAUXlKfN+C6e8Z68BK57MrrhnR1HwVoaW/duzlLY8bv4928qehrB75H+N9Fa98J7P4VtP4bFn4Gzbx+7jEwGl34P/nQ9zLt6Zs9xJzGTVomjT0atkd0AACAASURBVCrQdrm9KBVyjGplwta/qiiVJ96tCXssEAzR2N1P/qgCd1mGgc+tLkxIBvucdD11ne7JF4xArVRg0ihp7/WQPVhcHq25u59cs/RcYf9Bvu/4BIX+YNz7G42EDxcUBEEQBEEQEi8UCvH4O9V8/vcf8+/P7ME14J/tXRIE4XTVWSMN90o0c6FU1I5FTzMYBwvPGfPB3QlZi6Vu3eWfh+VfgKU302+ZD5bShO/yhLIWQfGF0kDDjijyhIeGkzXtmLzjedoLz61SJ7kh6xToeG6F9d8Fj0Pqeo1gfnYKh1t6+bCmk1XFM5MDLJygYDVU/i26ZW2HIT3GGIGcZdCye+LhotPFXgUZC6RzUaLVvwcLr4dvNMBl3wPFOIXT7MVQfim884PE78MpKkWTjKNfGjAYqRg8VSVpehx9Xuyukczo1p5+UrXJaJLDL5qk6pK577LERJQsKzDHPaQQpK7nxq6+cZ9v7OojN1ULfi9Jbfu5+opPopjmZhZReBYEQRAEQTgFyGQyHr9pOVv+80JChPjpG6fv0CRBEGZZPIWhaAxFbQxxNMBLd0AwQrfV9p9AvwOO/Usq8ILUIVlyISwZHPClUMIVPwG1EXfmKkif7huGR7noAZh7uVR4tkdxTna1Q+l6SFJLRfjx6DOl9T1+fvj7lUi9zVIHsSHz5O94drZLBfKlt0hxBBFkGFWEgD2NDpYXmGd2/wSouFK6A2Ey+zbDwechPcYinSETNGY48Hx8+zcV9mOQvwp8feCNrxN1XPXvQ8G50Q0mXXc/7PszOKah8/oUZNIq6RksPEsxG5E7fOMll8tYnG9mV303L+xqwuMLUN/ZR4ElsQXu0a48K3tKRey8VC1N3f3jPn+kzUl5hgHaDkBqEZevKEepOMUKz4899hirVq1izZo1w1+CIAiCIAhCYqiVCu65ZC4v7W3GO823xgmCcIZqPyR1F8fB4XHwcevH9Az0jH1yKGpjSPUW2PtHOPTX8OX8XnjzAXjqKmj8GFbcNvLc9X+ARdePWXVn+Y2w/uG49nnK0srAfnzy5VztkDYXvrR94sKbIVPKsrYdgeq3ErefQ0IhqcvZmH1qdDy72qT9XPwZqavW0wvbfoypauRzI5PJmJ9tpCLLiCGBt9sLUSo4V+oI7q4f+9zhl2Hro9DTJEXjWEqlCzax2vRHeO2bsPP3M9v53HlcurhkzIH2w/DCv0mfwakKBqTf8/xzo1tenyZ1frcfmvq2TyH7OvZxrHvshb0UjZKePh82p4fGrj7yzIkvCC/LN/PD145y7wv7+daLB3l5b3PcwwNnSp5ZS0NXH197dg/NjvACtC8QpMbukjLwGz+CvJUzsk/TMlzw7bffFoP+BEEQBEEQpkmRVUdxmp4tR9q5dEHWbO+OIAinE58HHPVSoSVGgWCAr275Ko4BB0aVkT9t+FP4AqlF4VEbTTukrOPXvwMBn3TLuSJJKjTqM0BrgdVfk4bxDRkvF1ahDF9uJpkKwG2ThgdOtA8uG6TkSvm2E0ktguK1UnGu9l1Y8W+J3d++Tmk/lZqZifWYKmc7GDJAnQJFF8DeZ+DD/4fWsiRssbNyTYSYhSgGQfq9LLsUjr0GZ38x/LmPn5AiUkIBqPgkXP6j+LaRMR9ueQVe/KKUE33Z96a+39GwH5c6ulNyYeeTUiHd74FPPR1dp3Ikx9+Q3itdmvTZjpYpX7pT5DTn8Xu49917sffbaXA2YNVYef7K59lt282fKv/E7Ytux6RRUt/lZvX3tmBUK/n6xbH/zZrM0gITv9hynM23r+LbLx0i26ji+xsXJHw7iZSXquEXW6qkIYJmDfdcMnInUK3dTXaKRooKafwo8jDLaZDwjmeLxUJS0qzPLBQEQRCEk8ahlh5+ubUq7gnFghDJdcty+du+ltneDUEQTjcdRyC1BJKSY37pn4/8GblMzmOfeIzO/s6xC+gzpWF2Q7erN+2Cc/8DLnlIGq5V+bL0eG+rVOS56a+w4NopHMwMkSukTOzOSbqeXe1SQX0yGjPc/JJUFKjbHjmKZCp6W8CQLf1bnyHtV6K3kSheNwT9oDJK3y//vNQNL5Oj6q0LW/TOi0r5+kWJLz4JUSq+AOreDX/M2Sbd0l+yVvodXzj2boWYpM+Fm16C/8/eeYdHWeVt+J5J7733hDRK6BCKIIgKCiIqggVdbKuu3XXVFT91m65r27WBupbFXlFAqkgntAAJJJDeJ2UykzqZJJOZ74+TwpBJmWTS8L2vi4uZt5xzMvNOe85znl/Kl8JBPdBoVFCaAv7jxXvS6e/EyoqaElEUsC+0NMOmx4RoPedP5p3rHiomBi8yDBc42D9N/xSdQccjkx9h2/XbcLJx4pHdj/D0vqfR6XX8kPUD7o42bE5RMCXMk8QoLyaFWj5iZ1aUNz89MJvJYZ5semAm/3VZi/cvj1m8H0sS4iGiNn4/J5KvjhYZrY5MV9SI/GiDYVAdzxYTnh977DEef/xxlEoly5Yt47HHHmvfJiEhISEh8Vvl51QFt394lDRFDYv/s6/TkicJib4yKdSDMyUWWOopISEhcT5lp3sVs1FYW0izvrn9vlan5f3U93luxnO427lT02ji/UkuF+5gdb5Yql5VIPoae71wFCqzxHG1JSJaYSThHd1z3EZdOTj79r5NtyCwdxeZ25akplgUZwSwtgN7V+GCHo7UlgpHaJuzNOJSMfbL/g/b2nwRV9CKtZUcayupjNWQEX4J5B0wnsQ4swFiF4mVCx4REG6BKFZHT5h4Kxz4T//b6onjH0Ps1eDkJYTnlkYYfY1wOye9C/mHzG/zzAYhIF/1Lxi/wrxz3UMuOsdzY0sjS39cSrmmHAC1Vs0nZz7hT1P/xFT/qTjaOPLo5EdRNaj49KpP+eOUP7IzfycuDtaoNc0snRDI2zdPYnSgq8XHJpfLGO3vDD89iNXHi0S+9tmfoa7C4n1ZikgfZ4LcHXj08hhG+ToZmVTOldYS5+8K1YXivbO7WgMWxGLvyitXrmTFihU89dRTPPvss6xcubJ9m4SEhISExG8RTZOOv21K451bJvH2zZOYGuHJ/szh+0VFYmQR7uVIabUWTZPO7HO/PFLA61JxQgmJ3zYnPjWdk9qLfOd9RftY9uMyfsj8oX3bppxNjPUeS6R7JC62LtTr6mk5TxRspy1uoyQZAsaLiAwAryhQZYvbNQqRPzyS8I7tucBgbx3P5xN5KWTt7OuoTFNyEvzOWy7uEtD/AoP6Fjj6Qf/aMEVdmXDKtyGXw+/3wcRb0dl5XJTuzxGLa4CIxyk73bEtcxvELRbOyj8c6Toqx1wS74dTXxpNPFiclmYREzLjfnHfLRgCJ4p4GrcgIaanfGlem3q9cErPeqhvY3IPu+iE59PK0+RW5/LlWfFY/pz7M7ODZhPmGtZ+zGS/yXx29Wf4O/kT7haOu707KdU/Yx/wDe/krCK3egCKsGbugHNbIXcPFB4Vz/et30H8Yjj5Wc/nDxH+bvbsf3Ie9jZW/GlhHC9tOdu+6vZsaS1x/s5w9L8QNqPvUTFmYjHhedq0aUybNo26ujoOHTrEtGnTWLduHY2NjZbqQkJCQkJCYkTxWVIBk8I8mBbhCUCcvyuZZXVDPCqJiwVrKzmRPs5klZt3Ta0/lMfL286xPa1sYAYmISExMvj1H8Z5y20oUoxFyVY+T/+ctafW0tzSzBN7n+CW+FvYnr8dAJVWxSdnPmHV6FUAyGVynG2cqW2q7dy+RziocqHoGARN6tjuGQWqHHF7RDqeY3ohPJvpeAbhrkzbYLyt6Jgo5NZXCpMgNLHjviVynktOwubHodHC33PaHM/nYyeKezW5hous3+6orxT/JAaHiDlCqAMhshYdh5Dp4r6VBSNZXQPFdTGQhfaKjoGTN/iPE/fjr4Fr13bsj5wHObvNazP9JzHZFn1F38bkHircqhcB32d+z5dnvyS5LJlLgy/l24xvadA1sDV3K4siFnV77jWR13Co4idiPUdxU9xK/n747+1xHY0tjRgMBv555J+cUfbx+ji8DjY+DD/eD3v/BdPuFpn79q4w6XaRMz+MkbUKypNCPbhmfCB/3ZRGtaaZEwVqZpR8LET1RS8P2ngsvg7lzTff5NZbbwXgjTfe4O2337Z0FxISEhISEiOC/VlKFid0OLZi/JzJMFMklJDojlg/Z86WmhB2uuGzwwW8dN04itWaARqVhITEiCBokij4dT66Jig5AcFTOh1eUFvAyfKT5FTn4Ovoy33j7yNNmcbuwt3c8NMNzAudx3T/6e3Hu9m5Ud1U3blfjwhQ54HilHAPtuEVBZUj2fEcDRXdCM/NDaBrEPnN5hA2W2TZqlonCQqPwMdXQ8pXPZ+bsxvKLojpaNEZi4EghOeafubltomNba51S3Gh4/k8Gl0jRCZ5d+x7FTbca9kxSXRN3NVClNPrRea5owc4+wxMX2EzIf/gwLQNkLdP5Fa34eAuMqbb8I0XBUWzfoH114n3T4DGWvFeYDDA+5dBees1qtfD7hdh3jN9d5o6+Yg+LT3BM8g0tTTx1om3eD/lfY6UHuHaUdeSGJDIn/b8ibyaPGYEzOj2/N+N/R1bbtjEj6vWcHfC3ai1arbmbWVr3lbmfT2P15Nf58tzX/LF2S/6NsDk/8HyT2DaPeKzKuHGjn3BU0QB3NpSkQGuG941fP54ZQzH8lXc+clRbo1pxvXEe6J+govp99WBwOLCs7W1NV5eXgC4uLggl0sZSxISEhI9UajSsPqjI0M9DAkL0qI3kFygZmp4xw/MaF8XMsvMEwklJLoj1t+VDDOE54JKDcq6Ji6L96O5xUCttrnnkyQkJC5OgqYIR9/5KE6CV6QQWC5A1aDinPocGeoMYj1isbe2Z1bQLP645488P/N5Hpv8WLvLCsDN1o3qRhPCc1vUhuKUiNpow8kHWpqgQS1iH0ac4zlaiK5dLf2vKwcnX/MFJytr4bQ80xpr8sPvhdNSndfzuXtfgdSvO+6rcqD0VGskgmfH9oi5Ine2P+TuBTu3nnOuzcWU47mVRreInh3PlZnC3VeaatlxSZgmar5w9GZsgaKjEDx14PoKmwX5Bwau/dy9ED6n6/0ymYjC+fo2MSGU/pPYfvo7+HqVWAFRfAyyf2ltb7d4bEYt6PuYZDKR8zyCXc96g56N2RsZ5T6KAOcAkhRJTPSbyAuzXqCmqYYrwq7Api2CqRdYy615NvFZXjn6Cq8de41V8avYkLmBNy59g12Fu2jQ9aK+TulpHMuOi9vNWjEJ6j8OLnkc7twBdi4dx8qtOq69z26Ar24RsSzDFEdba166LoF8lYYHdf+D2Y8OqugMAyA8JyQk8Pjjj7N+/XqeeOIJRo8ebekuJCQkJC46Nqcq2JuppLllmFYUlzCbs6U1+LjY4eVs174t2MOBKk2zJPZJWIxYf2fO9TCZ0ahrobRaC8C2M6VcPtoXK7mMIA8HqdilhMRvmaDJUHzceFv+AfGD2gQqrQplg5IkRRIxHjEA3D/hfj5e+DFzgjuLM252XQjPHuFCdNZUiniNNmQy8IyEyhwhPI80x7OtkxDPu8oc7kvMRhtxVwnxtK5CPG5T7xTCs0ZF8L7HTYsezVohhrU5nnVN8N48+HyFsdsZYPS1QsDta2yBrlGIjAk3QmVW39roim4czw3e4yD7V+Em7wplJky8BQ6+adlxSZhGJoNL/gi7/i6em6DOqycsRpvj2VRWfT+RtTSK1R9h3TtviV0knM9L34Qj74lt5enCif/r38E1CPL2i+0nPoOJq/qfq+seCtuegW/v7F87Q8BZ1VnmfDWH15Nf556Ee7gx9kYi3SLxtPfEwdqB/175X56c9qTZ7U7wncCckDmM9R7LfRPuY/eK3cwNmcs473HsLtzdcwMH38QntTVGpTxNrMCxsRcTBX4mNM2wWXDkAzExJpPDL38xe8yDyaxR3iQ9eSm2eXtgyupB79/iwvOaNWtYtGgRWq2WRYsWsWbNGkt3ISEhIXHRsSVVgd5goEQSgS4ajuaqmBbuabRNLpcR5etEphS3IWEhYvxcSFfUtufameLP35/m3k+FuLQjrYwrRosf8EHuDhSrpfccCYnfLIETRRGw85cJ5x8UYo4JKrWVuNm5sTN/Z7vwHOEWwVjvznnQAK62rqajNtzDoF4p3GQXro5tKzBYMwIdzyBynruK2+hLYcE2QqYLsb7gIAROEgJ9a1yJS8kBSPux8zlFR4VzvbxVeM7+RYxv8moYt9z4WGtbIWb3tThg0THh+A6abHnhuRvHc5NrhIiMSf6f6XN1TVBTAjMfGthIBglj4peIYoKnvx1Yx7NbMFjbm86q7yvFydDSjIMyFXzijJ2uphh7HdyxDeKWiEic8nTxL2iKeF3Of1ZM6GlUYvLowtdeX3APE6sXMrZ2P+kyzCjXlPPALw/wbOKz7F+5nyn+U1gSuYSPFn7Ufoy13BpbK9s+tf9s4rO8PEfkFstl4rPllvhbePXYq5TWl2IwGHjk10c4WHzBe4FeD1k7sa/KFJ9NpSnGq3FMET5bvB9PuBkWviSKDQ7zyA2rqlwx+dnTNT0AWFx4rq+v5/jx4xw5coSkpCSqqqos3YWEhITERUWhSkORuoFp4Z7kVUqZq4PJsTwVv19/rOcD+9J2vpopFwjPADG+LmRJBQYlLESQuwOu9takFJkQd4CfUxUkF6g5V1pLea2W0yXVzIgSkWjBHg4UScKzhMRvFztn4T4ua40gaNFBwWEINe3wU2lVJAYkotFpiPWM7bF5VztXahprOu+wsRduZlM/7D2jIGuncJm1FpAbUfjEdZ05XHQUfGL61q6dixB2j7wv8kXdQoQrXHGSRucQOPR2Z9dn3j5IWCEc0toasfw/4UaY/4xxbm0b0ZcLh3RfKDwMoTPBa5Rlozb0eiEC+cR1fcycJ2D/G8aZt22PhToX3ILAK1pEuDS0ahOKU+KfxMAgk8HVr8H1/+1ZwOsvPjGgtNBkh65R5Kcf+DfeaR/D+JW9O09uJSJxRl0GOXuE8Hz5X0Se/djrxYTTZ8tF/rVj598GZnPpU3DXL+A3BgoO9b+9QaBF38KTe5/k+pjruTL8yvbtMpkMT3sLPCYIsdlablzAck7wHG6Nv5W7t9/NB6kfcKD4AN9kfGN8ouIEOHlT5z9dTA4oToF/Qved+Y8D71ghPHtGiNtZOyzydwwYZWfENTMEWFx4/vOf/0xgYCCPPfYYQUFBPPXUU5buQkJCQuKiQdOkY82G01w7MYhIH2cKKuuHeki/KY7nq0ntQrDrLzkV9cT6dZ5RjvJ1Jqvi4hCeDQYD1Q1SbMhQIpPJuGpcAJtTFSb3f3e8iMcuj2F0oCtrd+cwIcQdexsrAClqQ0JCQuSxZmwTt4uOgkeoyTiIFn0LNY01TA+YjoutC36OPTt3uywuCMKxa0qQmnCziIZwCzbnrxg++MSKXNcLadHBqS9h/M19bztslhCTgyYLh7KzH2RsRxV3CzRrYPsaEbnRpBGZs8nrhcDsEwtFRyBjO4xe2s3Y44RbuS+uvaJjQhD3HiWyUS0VfVB+Buzdu78egiaJnN2dz4n7p76EL24StyuzhOgsl4s4hDb3995/de2SlrAMcjmMu0EIsgOJV7TI8e4PuibhSC48Il5X+9/AWlspVgeYQ/gc4XLWNYqVIw8mi9dq3NViUmbJv/s3zjacfcHJS7x/Z++yTJuWouQENHX+Pftp+qdYyay4Z9w9gz6k3439HbfG38raU2t5Y94bJCmSjGOgMnfAqAXUBV4inMsFST1PmMit4A+HxWcZiEmKrU/D62PFKo3hSNkZ8B2aKGSLC89qtZpVq1YRHx/P7bffTk2NiVluCQkJCQkA/rIxDXdHG55eFEeYlyP5kuN5UElT1KCo0dKo66IQUD8ordHi72bfaXuIpyOFqpH3PFdpmnj+pzNUNzSz6r+HeXX7Od7Znc2sl3ZJ4uUQc3VCAJtTFJ3iNvR6A8fy1UyP9GRahCefHc5n1ijv9v1S1IaEhASjl3bENGRug+grTB5W1ViFi60L0/2nszRqqVERwa5ws3Uz7XgGIcCMWdZ5u1cU3PMr3PJN530jAZ9Y047nrJ2iIJhvN87dnmjLmg2aLP73CIeCQ2jdY2D1FiH+HvtIxKeUpQnHZcRc8B0Dmx8XWbTdZUzbOIgl/KaE8+4wGDqKyDl4gLWdiBWxBLl7IaKb4m5tLHwRzm2B3f8U2beFSWJcykwh+IEQXMrOCJEx+9feFWeUGP54jep/vMumR+DjxeJ1OvZ6WPIGJdOeMV80D58l4hd844Truy1KaMHzcN06IUJbkqjLIGuYCc/f3d0psqe5pZlPznzCE1OfwEpuNSTDWhG3ggM3HWBW0CxmBM5ge/72jp35ByFiLrUh84SQ7OAhHM09cf7n4LgbIPF+MYHXNpk73ChPM51XPQhYXHhubGykoqICAKVSiV4vFcqSkJD4bVNeq+1y375MJQ/Oj8baSk64lyP5I1CQ7Aq93oBeb/liH5YkraQGa7mMQpVlxTdtcwt1jTq8nDp/wQw5L95A06SzaL8DyeniGv53KI+5//oVO2s5O9PL+Swpn+VTgnnqu5RuM4Ytyb7MCtbuyR6UvkYKcf4u2NnIOXWBez+jvBYPRxt8XeyZFuFJo07P7POE52APB4qkSQMJid82QVNAWy0Ky2XugOgrTR6m0qrwtPck1DW014WfuiwuCK2FmxxM77OyGbmO57aM5ws/E499KIqK9YewWa3L9lvFY48wkMlodIsUy/cn3iKKRZadEfm6Ccs7CmPVlcOC53ruw2+M+QUGqwpEca225yxwAuTuM6+NrsjdaxQLotV18Z3awR1W/ywE54m3iG31SiFIercKz35jhSiff6A1FzjPMmMcTBrrRGSKRAfe/Yh3ydkDu/4m4iqsbODwOuEiHncDWi/T2fXd4hoo4oJ84/s2HnMJnADKc6C3vIGmT2hrREZ/8v+M3gN/zv2ZKPeoXkU0DST21sIQdO2oa/nm3Dcdv10qs8AnBr2NE1zzH/FeYm7Uk60TJN4rMrwzt/d8/GBSdBx++at4//O9SKI2Hn74YVauXMnSpUtZuXIlDz/8sKW7kJCQkBgxlFZrWb7WdPZWabUWTZOOKB8nAEI9nci/iKI2/rY5nff35Qz1MLpE29xCgUrDlDBPClSWfdwV1Vr8Xe2Ryzs7woI9HClSa2jUtZD4j1+o1Y6MqIoClYbrJgXzxJWx/OemiXxz7ww2PDCLZ66K52xp7aBlBX99rIh9mRWD0tdIQSaTsXhcAD9fELdxNFfVnjM+JcyDqeEejA1ya98f7OEoOZ4lJH7ryOUw+lr4/h5RhC14isnDVFoVng7m5XC62blR0/QbW/3q6Am2juKxbKPsDChOirzl/rZ9w4cd993DwTMKQ6uYgn+CyEMuTzPO8RxzHSz/WIhiPeE3RogT5lB0VFw3be6/iavg+Efdn9Mbmhsg/xCEXwLA1+e+5ndbf9f18R7hsOoH4fT2bnWel6eJ29D6t6VB+kaY/DuoKhw+gl1vydwGW6QoUyO8WuNdzMVggK9WiUmZG9eLiRkr2/4XQ4xdNLAFFc/HygbsXEVMyHBAcUqsyJBbtxfzVGvVvHvqXe4Ye8cQD66D2UGzadA1kFyeLGJBNJUiN98SjFogJsx0jZZpzxKk/wiH3hKfS15RQzIEiwfuzJo1i23btqFUKvHz8+vVMiwJCQmJixUHWysq6zpn5VVrmjmWr2JymEf7+2SolyMFKg0Gg+GieO88WahGLpPx+7lD8wHXExlltUT6ODPK15k8pWWd5oqqBgJMxGwAeDvbom3Wk1JUTY1WR36lxkgMHK7kq+qJ8Hbilulh7duc7cTXiBAPB0prtIR4Og7oGJp0enafLcfL2cJLFS8CrkoI4M6Pj/H0orj294+jeep2h7OLvQ3f3DvT6BwfZztqtM1om1vac58lJCR+gyx4rsOh1cUy6DbHszm42rp27Xi+mPGOEaKnW5C4f+DfMP33oqiiJQlIgJrijvu+8aDKEU7y2Ks6trsGiH+9wX8cHF5r3jjObTEuSBl3NWx5EsrP9i9a5Mj7EHEJOHmj1WlZl7KOuqY6CmsLCXHpQSRqy7WuONcRTeI3Wojk9eVw248iy7VWMbLc9Tl7xPgb1EM9kuGDa7B4PBrrzHOpqnNF0c5r/tOx7ZGU/sdhXPn3/p1vLs6+UF8Bzj6D268pSk5A4CSxGiP5ExTekTy17ymuCL+CGYGmi9YOBXKZnFvib+HTtE+ZHLdaFIG0VASIk5eI28jZDTGmVxANOsXJsGydWJliZTMkQ7C443n79u1cccUV3HfffVxxxRUcOHDA0l1ISEhIjBhc7a1paG6hSdcRO5RTUcf0F3fy1dFCJod1/IhztrPG09GW//yShbZ5hDkwLkCvN3CutJY0RQ3VmqF19FbUNpqM/EgrqSE+wIWwVsHfkpRUa7sUnmUyGcEeDuxME/mHeSPE5V6o0hDahbDs72ZPWU3XkTK9pblFz66zXedCHs6tJMjDgZJq7bCPcRlsYv1csLeRc7KwCoCUoir2Zym5JMa7y3PkchkBbvZSRreExG8dGweR9dxN4bm+CM/dFhe8mDk/rkLXCGc3m1+krDfEXAlL3ui4b20H3tEibsOvj8up/caKrOh9r4qCiD1RkCScjZNv79hmZQNjr4Nzm/s2hmataPPAv2H+GgC+z/yesV5jWRSxiB35O3jt2GuU1ndTwMsnTuRdh0zrEPwdPODuXfDAMeGO9gjvddxGs76Z27fcTnPLEK9Sy90D9m6g7Gem8cWEXC5yeVVmup4VpzoXkHNwt9y4BgsnHzEZMRxQnITAiZCwkszs7dyw8Xom+03m4YnDLwVhceRiDikOUVee2hHHYymm3Q17/mm5Iqv9Qd8CJSdFVv7oa4ZsGBYXnt955x2++eYbfvjhmWgKDQAAIABJREFUB7744gtef/11S3chISEhMWKQyWS4OdhQ3dDxRbVI3YCNXM6+TCVTwj2Mjl9/13SO5ql4Zdu5wR6qRSlUa3BzsGFahCf7soY2FmHFukN8dayw0/bkAjUJQW6Eejq2R5wYDAaLZBWXVjcQ4N5FdiUiW3dHWhl21vIRU1Ayv7Jr4dnXxZ7S6v4Lz0fzVNy7Ppn6RtM/dr85VsSS8YE421mjrB9GS9iGATKZjBunhPDajgyK1Bru+zSZfywbS4Bb19chSAUGJSQkekdlQ2XfhOffouM5YLwQtUCIuN7RIiZjMPAfD47e3RcR7A63IFHY8eTnIgu5J3Y+D5e/IJyj5+MRbhw3Yg67/go/PQgzH2jPyj1Wdowrw69kQdgC3jrxFh+d+YgkRVLXbfjEQHUhRM4z3h40qcPxZ4bwfE51juTyZJQNSvP/HkuhzhexANFXQGUfM40vVnxi4NcXxeutt5gSnkciTj5QN8QRdOp82POymIgKnAhOXpwKHst8h2AemvTQkBUU7A5nW2cm+01mb9F+8Iq2bONjb4CWZkjbYNl2+4IyE5y8B+8zqAssLjy7u7vj5eUFgLe3N87OZoZyS0hISFxkuDvaUKXpiNsordFy+Rg/3ls1mYkhxjPrUT7OvHrjeL45XmQRB+lQka6oIT7AlUtjfPj6WBGNuqFxcBdXNVCg0vDBvhwjh6zBYGBvhpJLYnwI83JqL+r4718yeWtX/10kJdVaArtwPAOEeDqSo6xnbozPiMj1NhgMFFRqCPMaWMfzsTw1TS16knIqO+37/HABZ0qquW1GmCSWdsGdsyOo1epY9MY+Vs8KZ+HYnpdWB7k7SI5nCQmJHlFpVXg5eJl1jqutKzVNNYNWfHbYEDBBOP8A8va1ZxQPTt8JIlKilS25WzhRfsK8NkITIXw2KDO6P66uXGQmj1nWeZ9LQN+F54IkWPIfmP1o+6a0yjTiveKZ7j+dZaOWsXrsajLVmej0OgprO5sL8GmN+Iia13lfGx4RoMrt1ZCOlx0HoKJhCAW+vP3iWvKO7fm5+a2x8CUImwFf3iKKpfYGxSnxehnptEVtDBWNdfDFShFrEzJdTLQB2d4RRFbkDA/XbxcsCF3ATnWayAm3JHI5zH0SksyMLRoIio93xA0NIRYXnp2dnbnzzjtZu3YtDz74IFqtltdee43XXnvN0l1JSEhIjAg8HG2pOs/xXNZaeO6KMf5YW3V+G/ZztWf55GDe2zt8C/P1RJqilvgAV5ZPCcHBRs6N65JQ13fOuh5oDmVXcsUYP+xtrNiZ3hHhkFVeh5VcRqS3E6GejiiqtKjqm/j8cAGfHs6npZ8xDoqqBvy7cZoGe4h9C8f6kzcCHM+1jXqQgZuD6VwwP1c7ymr670A+midyz/dkGH+BXn8oj9d3ZvD+bVNwsbch0N2ekqqROzEzUFhbyXnr5om8dH0Cd10S2atzpAKDEhISPbEjfwenKk6Z7Xi2tbLF2ca5+0iEixHvGCG6amsgd59Y4jxYJNwIC//ZfndD1gYOKw6b3453rBCSuiPrF4icYzoz1DWwb8KzrkkUBDzPiVrdWI1aqybMNQwbKxuenfEsU/ymkKnO5JDqEA/teshE/0Fw2XPg203kiBmO5xPlJ7CWWQ+t8FxdJAqDeUcLF6NEBy7+MOthiF4gnLc9YTCAIuUicTx7D23Uxr5XRGHT6z+A5R+1ZyXn0kRkUyOUJA/d2Hrg0pBLOaRT0+gRbvnGo6+AyiyRuz+UXKzC82WXXcbixYvx8/Nj/vz5rFixgoiICCIiIizdlYSEhMSIwN3Bxkh0VdRo8e/GDQuwcloIW1IVI9Yl1OZ4drKzZu2tk0mM8OSWDw6jaepFXqAFOZitZGaUN3++Kp4//5DKmRLhgtiTUcGcGB9kMhkOtlYsnRDIPf87hq+rHX6u9uzP6t9SSkU3Gc8AIR6OuDnYMD3Sa0Q4nhV1zYR6OnZZ9NLP1Z7SfjqedS16ThZU8fjlMUbCc56yntd3ZvLdvTOJ9BGrqILcHSmuGv6C/VAQ7OHI1Qm9LCIFBHlIjmcJCYmuKa0v5bkDz3FZ6GVM9jX/x+u8kHlsz98+ACMbxlhZi4zlgiRRbCs0cfD6dvAwcjxnqjOp0PRBLPWJFQUSNSooOm76mKwdQlwxhWugKNxnBmeUZ6D8DA0eYTRad4jZ51TniPWMRS7rkC5iPGLIrMrkVPUpsqqyUGlVxo3JZHDJY8J52BVtz5Gue2OEwWDgRPkJEgMTUWqU5++Aw++BXt/1yZZEUyliVLxjJMdzV1z2PJz4tGPSo+i4iIOpab0WS09DvVKI+DKZcOaPdJz66HhuaYbydONt2moitt3W++KVBgOk/QiJ94nH8zxyqnOJjFsGxz8xf2yDhIetG6HNzaTJB+D3qbWtyLpP+drybZtDSfLFKTwvWbIEnU5HYWEhgYGBzJ07l2XLlrFsmYklOBISEhK/AdwdbanSdHY8d0eUjzM21nLSFDV96rNFbyCtpG/nWoKs8jpi/IRIKJPJeGpRHNZWMlKLBi/rsUmn52BWJTOjvJg1ypsnrozlr5vSADiQpeSS6I6ia48siCG1uJplE4O5flIwPyQX9avvkqoGArvJeE4Icefm6aEEuNpTpWkedEHeXBS1ui5jNkAIz+X9FJ7Pltbi52bPjCgvNE0tFLQ6wTPKahkf7Eboef1LjmfLEeTuQJFaEvElJCRMc1hxmBmBM3hg4gO425tfeGthxEK25G4ZgJENcwImwA+/FzEUF+YfDxJqrZqKhgrKGzq7IY+VHuORXx+hRd9FFJpPa5zDsf/C5kc779c1QvYuGHW56fOdfIVo3ctifMoGJSs3r0SR9yv/dHfhg9QP2velq9KJ94w3Ot7P0Y9GXSPHq44T6RbZHoVhFv5jRYb0if+1b8qpzuHdU+/y2vHX0Bv0KBuUvH3ybeys7BjnPc7Y8VyZBVueMFtg7zOaSnD0EoX01PmgH97fHYcEZx8YfxMkvSMmBDY/JsTVdXOgthS+vAkOr4PiYxA0pZNYOiLpS8azMhNeGw0fLYIfH+iYfMn6BfuqjN6LxRVnxbkXOMc1zRrUWjWBU+4ROcclJ80b32ChymaC3pqTNQPkSh53oxDmh4pmLZSfBf9xQzeGViwuPD/33HOUlJRw4MAB6uvrefLJJy3dhYSEhMSIwsPRhqoG44znnhzPMpmMBfF+7Ewzf+mUwWBgzYZUrn3nANrmwc9W1usNFFc1EOzRIRTKZDJCPB377YrtLer6JpavPcjEUHcivJ0AmBnlTZ6yTcysY0yga/vx/m72/O+OaaycGkJipBcpxX0XyEuqGrCxkuPhaDqWAoTY9+TCOORyGaGejhSohrfwV1rbTEgXhQUB/FsdzwaDgeYWPQezzXeMbz1dyqwoL2QyGVPCPDheINxL2RX1RPkY14sI9nCgSIqHsAjBHlJetoSERGeaWppobGnksOIw0wOm97mdaf7TUNQrKKgpsODoRgCRl0LoDFj8+pANIVOdiYO1g7FLt5VUZSq7Cnbxzql30BtMOHZdg0QhuxOfCpeotgZSv4WGKrF/zz8hbBa4duEYtbIWEQC1HTEreoO+y5V82VXZAGwp3M32FjVplWnt+9rync9HJpMR7RGNo5UjS0ctJakkiWcPPGt0Xq+Y/wzsfRX0LWzI2sDqraupb6pnU/Ym8qrzeC/lPdJV6bx0yUt4O3ijPPuTyFoGyNop/u9lXEe/0VSKAmE29nDdeyAbfgXbhgUz/gDJ62HTI0JYXvmFmAD6aBE0aaDgkChCGDxlqEdqGfqS8Vx4WOSfP5IqinDub43FzdhKVcQSOPJe7yaNzm6CuKs7Cfi51bmEuYZh5RYM17wFn14PR94XE1bDieJkJriEc6ri1MC0H5AAldk9rqoYMMpOg/cosO36N9xgYXHhuaCggIcffhhbW1vmz59PbW2tpbuQkJCQGFG4O9qg1jSTWlRNRW0jZTU9O54BFsT7setsWY/HXcjPqaWcKKgi0tuJEwVVfRlyv1DWNeJqb42DrfEXYn9Xe0qrLSs86/UGkz9i9mUpcXO05Z1bJrXHQwS42aOqb6KuUUdFbWMnR/L0SC+c7KwJ83KkSN3Q55znY/lqJoV5dBlLcSHxAa4cyVX1fOAQoqjVEebp1OV+JztrbORyahp07DlXwUNfmFfISFnXyKeH87l7jsglnhzmQXK+uHZzKuraIzbaCHJ3NHLpniqs4kCWEoPBwMI39lI4zIX84YS/mz3KuiaaWwZpqbDERU1do44//5A61MOQsAAvHnmR1VtXk6RIYkbAjD63Yy23ZrLfZM5UnrHg6EYA8Yvhps/FcushIrMqk6n+U006ngtrC7lr3F3sLdrLNRuuoVxzwTEymcgSbm6AkGmQsQ1+uBcOrxWiXfL6nkV1lwAjN/BLR17ioV8foqmlswiTXZVNkHMQ7zXk4mrnylnV2fZ9Z1VnOzmeAaI9oklwS2Ca/zS+yfiGgyUHef24mUJ/4ESQyaGqgE3Zm3h+xvP8ceofmeI/hVMVpzhZfpK7x93NJL9JeDt4U1GnEI8BCOHZ3h2q8s3rs69olMLxDDDm2ovDrTsQuIfADR+KlQbXvCniVuY9DS06WLZOxN/kH4TgqUM9Usvg5G2+8FxVIDLO7Vxg6dvCBV5yEjJ3UDHmTrESYPNj0NWKiDYyd0Dswk6bc6pziHRrrTUy+hpY9b1w/m57xrxxDjQlyYwPnM7J8pMDE29pbSeuR1W25dvuDcMk3xkGQHhuaWlBpVIhk8moq6tD3l2ukoSEhMRvgLaojZe3neWDfTlUNzTj5WzX43kTQtw5W1qLzkxB6GC2khunhHBprC9JOZV9HXafKVQ3EOTReWY1wK3/OcAX8tqODN7+NavzGFQa4gNcjMRfays5/m72HMmtxM/NDhsThR0B7G2s8HKypaSPubfH81RMCfPo9fErp4bwWVLBsM7zLm3NeO4OX1c7ymq17EgrQ1nXRENT79327+7OZun4wHaX/KQwD47ni3y5HGU9UT7GoneUrxP1TTo+PpDLP7eeZdV/D/Psj6dJU9RwtrS2PctbomdsrOR4O9tafFJI4rfJWUUNXxwpGJLVNhKWQ61Vsy1vG172XtjIbQh2Ce5Xe36Ofp2FTYkBJ0OdwczAmai0qk6RGkW1RUz0ncjXi79mvM94tuZu7dyATxzEXyOczTufg8AJwrX4w71w1cvCadkd5xUY1DRr2JS9kebmBl4+2rn4W051DitiliMz6Llt9G00NDeg0qrQNGsoqSsh0r1zwdy7x93N8qDlxHnGsSJ2Bd8s+YbiumKOKI70/kEC8IlFU5ZKijKFaQHTABjvM57DpYfJq8ljtJfIzPaxcaGCZsjdK6IuCpIgYcUgOp5VHcKzRPeMugyu/HtHxICDBzx8ShQf9IwExUkImjS0Y7QUTr5QVy7ylnuLOh/cw8Rtt2C48h+w/lpwC0Ln5A/LPwZVrljZ0BUtzVCaalLAP1523HiVQsB4WLYWUr8RrvPhQnEyAaFzsJJZUVxX3KcmtDotygZlN7FFcSKSZCi4mIXnRx99lJtuuonTp0+zYsUKHnjgAUt3ISEhITGicHe0oUrTREZZLd8cL8Lb2Q4rec8uBQdbK3xc7Cg0cxn88Xw1k8M8SIz05NAQCM9Fag3BHp3zjf1c7SmzsPC8M72Mzw8XoL/AnVyo0pgUSkM9HdmfWdmtexcg3MuJvD4W/TuWr2ZKeO+F5xlRXjTr9RzN62UhjyGgtFbXo/Ds72ZPSVUDv5wtx9Xeute5wYrqBr5LLuIP80e1bxsT6Equsp76Rh3ZJhzPjrbWfLJ6Gu/szqaitpEdj82lsVnPO7uzsZbLyCyrM/+P/A0T5uVEdoX0mEn0n8zyOgwGyFUO/6KpEl3zbca3LAhdwCuXvsK6y9f1egVPV/g5+lGmMX8Fl4Rp1uxfQ0pFCl+c/YIn9jxh8pjiumKOlR4j3jMeV1tX1I3G3zEKawsJcQlBJpNxRdgV7Crc1bmReX+GS5+CsBlQUwxzW28HJIjogp44r8DgjvwdTMSex5sdOFLaWRjOrsomzsaD1+rlLI1dToxnDOdU58hQZxDlHoWNvHN8mZ+TH562nljLrXkm8Rk87T1ZEbvC/GKWPrEcL9zLaK/ROFk7gkbFOO9xbM/bTqxHLLZWwrXuo61HaW0L8Uvg7WmisGJAghDxBhqDoSPjWaJvtBkiw2aBT/yQZa9bHFtHsLKBRjOSBqoKwD204/6Em+Dxc7Bqg7jf5hY/8r6I2TFF2RkhXl/wOFY3VrM9fztLopYYH+8WLOJN0jf2fpwDQdJasWKjpRnKTiMLnMAE3wl9yonXG/Ss3rqaa364hqf3PW36IJ9YqDjXz0H3gSaNiAUKGh6RMhYXnqdOncq2bdvYtGkTmzZtYubMmZbuQkJCQmJE4eFoS4FKQ61Wh1wmw68XMRttjPJ1Jru894JQjbaZApWG0YGuTA335HRx9aA7z4qrGgg2UVjP382yURuVdY0UVzXg6mDTydldoNIQYsJ1HeLpwIEspVGhOlOEezuS1wfhpK5RR05FPWOD3Hp9jkwmY9mEIHaklfZ88BDQqGtB1aAj0L3763bWKG/+9G0KXk62TAj1oLAH4VmvN7D1tIIXfkrjpmmh+Lp0tG9nbcXoQFd2ppeh1xvwdu68VDnSx5nDf76MV5aPx8/VnsUJAWxOUXDNhEAyzHjNSMD4EHdOFg5+LI/ExUdW62svp0ISnkcym3I2sTxmOXZWdoS7hfe7PR9HH8nxbCEMBgPb8rbxyK+PsPbUWk4rT5NclWx0TIWmghs33sjiyMVM8J2Aj4MPFZqOpfg6vY4yTRmBzoEAJAYmck51DpX2gtgv91CxjD9kOoy5TuRWX/c+LHuvd4N1CWh3PG/K2cS19Q2E5h2iuLaYZr1xfmxOdQ5RTU3M8IjDycaJOM84zqnOka5KJ84zrtePz2iv0UYxHb3CJ5ZDladFpMy5n+HzFe19TvCdICIalFl41ZahlhtoWfAXuD8Jln8k4gpaHc9nlGf4LP0z8/ruCmWmiIRoo1kDyChtruGpfU9Zpo/fKmOWwaTbhnoUlsXcuI2qAvAIM95mbSfaacMzQjjHj3Txei9JNumm3ZC1gTnBc/B28O58zoRbIOWr3o9zIEh6Gw78G16OBI8IsHdlVtAsDhQfMLupnfk70aNn5/KdHCs7Zjpj/nzHc85uOPl5/8bfW7Y9LSZZfDvHFA0FA5aD8dRTT/V7dnzIGW7h5xISEiMSd0cb0hU1RPs6c/loPwJ6KCx4PlE+zmSZ4UQ8WVDFuCA3bKzkONlZE+njxJmSLmaqB4gidYNJx7OlM56TclRMC/fkhsnBfHu8yGhfQReO52APR86V1RLWg3tXOJ7NXwp2uriauAAX7KzNK/gS7OlAee3w/MwpVjfg7WiNdRfRJG3cf+ko/m/JaB5eEN2r4n/ppTU89X0qTnbW3DsnqtP+lVNDWLPhNJE+zl1+nzh/+5Lxgbg72nDL9DAyy6T6EuYwQRKeJSxEVnkdsX4u5EgO+hFLaX0paq2aMd5jLNamr6OvJDxbiKrGKmytbLlj7B38ddZfeXr603xWaCx2Hig5QGJAIvdNuA+5TI6Pow8VDR2iVGl9Kd4O3u1OXjsrO2YGzmRL7hbTndq5CJHVyhpsHMT/veG8qI2cqhzGKguw06jws/ekuLZjWbtKq6K5pRmfqmLhDgRiPWI5U3mG9Mr09qiL3hDnGUemOrPrZe/nUVpfyrcZ32LwimFPYymzg2cLN2ZVAbZWtoz1Hssk30mQ/Qt8fBU2yixc5LYcqc7gjL71O6J7GDm1+bxy9BXu23kfb554k7qmPrz/teigqNVxaTCIOJNdf+/Y3+p2TqtMY3POZpQN5hdxlmgldDok3jvUo7As/gmi+Gcbpq7/tigOXRPUl4NLYM/tzn0Sjn4A29eIYqPnU3zcZFzJT9k/sTxmuen2gqdAeXrP/Q4UtaXCGf6Hw/DQCbhLFAidGTiTQ4pDpgutdsO7p97lwYkP4mjjyF3j7uKVY6/Q2HLB77k2x3NVAXx3F2z7syg4OJAoUiBjO1z96rDJgh8w4Xk4Z1X2iuoieDkKCs3MiJKQkJC4AHdHW/QGiPZzYfWscFZOC+35pFZ6cjzXNeooPi+LuC1mo41YP9dBF+GE8NxZ2PV1taOirrFTLEZfOZSjZEaUF9dNCmZnehmVdeKDvrlFT3lN5+KBQLsYHdaD4znMy4n8PkRt5CnribogFqI3eDvbUTHMhOcmnZ71SflkltcR4NJ5iaspFicEctW4AEI8HHss8Feo0jAlzJNXbxyPm2Pn9pdPCeGu2ZHMiOrdstKxQW7sf3I+owNETIe52ei/ZSaGunOqsIqs8tpOkzgSEr2hvlGHrkVPVnkdV4zxI0eK2hixHCw5SGJAInKZ5X4mShnPlqOkvoRA50BuHX0rc4LnMDtoNspGJVXajsnDgyUHmRnYserYx8HYcV5YW9gpt/vuhLt5L+W9dmf0WdVZapr6aVxwC4GqApr1zai0lfi4hUHMQsJl9uRW57YfllOVQ4R7BDJlhnAHApcEX8LR0qPsKdpjsrBgV7jYuuBp70l+bc/xF/9N/S9/P/x39rVUYWhpYrRbjCiiWF8BuibevuxtLg25FBSnoK4MTnyKt60bD//6MPftvI+jpUfBNZAXHAxom+v47KrPmOQ7ib1Fe81+qCg4CB9eKZzOZzeDtlpkSOtaCzFqKsHRk8LaQgD2F+83vw+Ji5dF/4Sj70PBYSEI/2sUlJ3nvtVWwzuJ4v+aInDx790Eknc03LsfasvgzSnGgmlxcifhWdmgRFGvYLzPeNPtuQZDg2rocp4Lj0DwNJBbCXe3rfg96O/kj7eDN9n1vReEi+uKUWvVzAqcBcDymOV42Xtxx7Y7jAuoekWLx+2jq2HWIzD7Udj4cMdreyBI/QbGrwR714Hrw0wGTHheuLBzdcsRxf43wDcONtwnqvlKSEhI9BGPVlEtxs+ZGD8X5sb49PrcnhzPf9+cxpofUtvvJxcYC88xfs5kdJF3e6JAzQf7cjpt78vE4do92by2IwODwUCxWkOQCceznbUVrvY2KOuNBdZqTTP1jTqz+zyYXcmMKC88nWxZNDaAL44UAKCo0uLjYoetdeePuJBW4Tm0h4znCG+nPmWU5lbWE+Hdfdum8HGxQ1lneeG5ryK/trmF1R8f4cWf03l9RwYBLr10N7US4ulAoar7z85CVUOPudEPL4jmyYW9X2LrbGeNg60Vfq725PcgfEt04Odqj4ONFb9ff5xPkwYhq1LiosFgMNCiN3Dz+0n8+YdUKusbmRPjI2WGj2AOlhxkZpBloxLbHM8j3pg0DFDUKfB38m+/L5fJCXcKJ10lXIR6g56kkiRj4fkCx3NRXRHBzsbCc5xnHNdFX8fLR1+murGaO7bewaLvFrGrwET2c2/xGgWqbCo0FXhZOWAdOBFGXUZEQy25NR3Cc4Y6gxiPGLEcvVV49nbw5m+z/0aDroFoj2izuo33iie9Mp0zlWe6PEatVbM5dzOzA2ez5uhLLGw0IEv5UuTQuvhDXSkutq1FqktThFhVW8J0nwmsSVzD8zOf56UjL4HcijxbW+4NXUiIawiXh13Ojvwd5j9W6jzhJv/6Ntj4EFz1L+q9Inj3wAusP/MJ+voKcPKmoKaAeM949hXtM78PiYsX10BY+g58eTN8ewdEzYdvV3cIvAWHxevr9Het+c5h3bd3Pk7ecP37MO56OLFebMvZIwoa+hqvjElSJDHVbyrW8i5+N8jlom91run9beTug8+6cE33h6IjENK5GCLA7KDZHFMf63VTRxRHmOo/tX31pY2VDS/PEYVTjXLsbR1hxh/g2rdh5gOQeD/Yu8HXq0w703tLY53x+cpMIXDrW+DMDzDuhr63PQBYVHhOT0/nww8/5N///jdNTU2kpKRYsvnBo75SzBKs/FzkNqX9ONQjkpCQGME42FhhayUn2s/8IhZtjmdTP9YKKjVsPKXgdGuURovewMmCKiaGnic8+7uQUVaLorqBwxfkIB/IUrLltHGu8DfHClny1n7U9ebNwm48VcLnhwt49sfTFFc1EGTCbQytBQarjQXWv25O43+HzBO7ymq0qOqbiPcXM7m/mxXOp0kFNLfoRb6zp+n+24TOnjKew7wcKVI30KQzzzWbr9T06KY2hc8AOJ7PltYw7R87OV1cbfa5W04r0Ovhi7sTOVtai38vHc9tBHs4UlTVvfDb3fPUX2L8nDmrkOI2zGFCqDs1Wt2wc95LDF9KqhqY8JcdPPLVSWQyGVtPlxLu5US0rzM5FfWSyDgCadG3kKRIElm3FsTe2h4Ha4dOBe6GCoPBQFFtEedUQ1DwqZ+U1JUQ6GS8RD7CMaI91zhdlY67vTsBzgHt+30dfNudzMoGJXuL9hLiEtKp7XsS7iG5PJm/HPoLl4ZcyquXvsqrx15FpzffHACAsy/omihVZeJvkEPgRPCJJ1xTTV51Xvth59TniHWPEcKJd0z79tlBs9l9427srXsfUQdCRH/zxJus3LTSKNv6fL7L/I75IfO5b8J9qBvVLHKLh53PwdS7wDUIqjuiQFCkwOUvgI0jT858jmuirmFG4Azya/JRaVVo5XK8PlgI+19nXsg8khRJaJrNnPxW58O0eyBiLtz2E0TNY6//KHbmbePDwy+TU7gfHL0oqC3gprib+hQLIHFxkVqRysO7Hub7zO/FhpgrYNlakcd+/QciY/3cz2JfwUFRZC55vbjWzBGe20hYCanfiaiM7+6E5R+DtXENlkMlh5gR2MPnh1dUz1ETB980zjjvD411cPAt2HC/iJ8InmbysBtibmBnxc72GJsfMn8guSzZ5LEAR0uPMtXfWMSWyWTMD5nPnsI9xgcveA4i5ojbVjbisVPlCtemgLwHAAAgAElEQVR4X9lwL2xtzXtvqof3L4P/LYU3EsDWGfwsF5dlCSwmPL/11lu8/vrrWFtbExwcjJWVFW+99RZvvPGGpboYPGwcYOVn4sMy9ioRAi4hISHRR2QyGaN8nRkTYP5yF08nW6zkMpP5v+/vy2H1rHB0LXrKa7RklNXi42qHp1PHl4BYPxfOldXy3t4cXt2RYXR+dkW9UZyErkXPm7uyCHBzYPXHR3stGlQ3NJOnrGfjg7Ows7YiMdILJzvTM93+bvYoqo2dsEfzVJTVmJf9fCi7kukRnsjlYpY5PsCVcG9Htpwu7TLfGYT7/JM7puHcxfjasLexIsbPhVNF5uXe5lXWE+5lvuPZw9GWWq2OZgvGQ6QranC1t+Gm95KY/o+dHMjqfR7ghhMlrJwWwvgQd1bPCmesr3k/+kI8euF4Vnf9PPWXayYE8cbOjEEvrDmSuW/uKD64bQoVtY2SYCjRK04UVBHu5YiqvpGXb0jgiYVxTAn3wN3RFltr+bDNrZfomrTKNHwcfPBz8rN428Mp5/m7zO+4afNN3LHtjl5lAQ8nFPWK9qKAbUQ4RZBeKRzPG7M3clnoZUb7w9zCSK9Mp0Xfwq0/34qnvSfLopd1atvB2oE/TPgD2/O3c/uY20kMSMTH0afr7OeekMnAKxJFeSoBjQ1CePYII6Kmwihq45zqHHG2HuDg3mlpuKON+d8TEnwSaGppYrzPeI6XHzd5zPa87SwdtZTRXqP57KrPiL7le/hTDky+vTWbulV4bqgSMRch0+GxdOFURDxWfo5+7CvaR5hnLLLVW+D097jLbRmnqWN//i/mDboqXzjEF70E/mMBOGxnzXWqcqYa7DiTuRkcvSisLWSy32T+NutvyBge2a0Sg4/BYOClIy8R6hrKa8dfQ1GnEDuiLxcip0wG8Usgc7vYnn8I5j0tYmR+eQG8Is3v1G8M2DrBR4vg8r9CxCXtu1r0LWzM3sj+4v09C8+ekaDqRniuzBZxIY21QjTuL/tehcxtImvZxc9kQUSAMNcw5nnP44VDL/BD5g+8deItHt39KBuzN9Kib+FfR//FM/uf4fP0zynXlHO0rLPwDDA3eC57i/Z2/13aygai5kFeH1cu6PXCFZ76rRDoz24W2eWPpIrJh8Wv963dAcRiwvPBgwd57733uO2227j++uu57bbbWLduHQcOmF8dcsixdYTw2eJ25KVCeJZ+hElISPSDnx++BF9X88S7NiaEuJOc39kltCejgsUJgYwJdONMSQ3H8tVMPs/tDBDgZo+2qYXvk4tJK6kxil7IKq9DWddEXaOOhW/s5cZ1h/B1seO9VZNRa5o4XSyc1Hq9gRqtcfXx8zmer2JCqDsBbg48u3g0H682PZMMQnguOC8CobxGS36lxuyYiUPZlcyMMq6WvHpWBB/sy+HHk8XEdOEul8lkvY46mRnlxcGsyp4PbMVgMFCg6pvjWS6X4elkS2Wd5fK+civqWTw+kL1/msfN08LYlFLSq/MqahtJLlBz+WghPDy3ZAzxZgrPnk62NLfou71uhON5YITnJQkBxPi58J9fMtu3/fGbU/x0qnePwW+RccFujA9xx85GTnVD18+bhEQbKcVVXBbvx2d3JRLj58KqxDD+ulQIJomRnuw6OzxERonec7DkYM+iQR8ZTjnP2/O28/zM5/F19O02jmE4oqg3jtoA4XhOV6VT11THxuyNrIhdYbR/it8USupL+C7zO1xtXXlh5gt4Oxh/h2pjadRS3l3wLrGeosjfHyb8gTdPvGm+g7cNr1GUlqfg31AjCqDZOhEhcyCnKgu9QY9OryO7Kpvopqb2woL9JTEgka3Xb2VB6AKOl3YWngtrCynTlInCgQih2gi3YCE8GwxQdhp8R4tMWAd3o8Oi3KP4peAXwl3DRdE0dR6kfMWCahU7szd2PcAmDWRfEGGizgcP4ULdmL2RLHUWSfWFTL/uU8aEXsqZxkqaHNwp15QT4BzA/ND5XRZevhgxGAyotKqhHsawYXv+dpr1zTw6+VEuCbrEdK549BWQtVO4YUtTICQR7tkNd2yDmQ+Z36lMBrMegksehwk3Ge36peAX3kt5j/vH30+oSw+1jDwju3c8p34LCSvAPVTEgvSXnF9h7lMw62G4fSPYdV2L54agG/Bz9OM/J/7Da/Ne44MrPuDloy+zLmUdJ8tPMtlvMqeVp7n2x2vRG/TitX8BUe5RyGQysqqyuh9X2CzI76NWWpEOjp6w8EX49k449iGMu1E8RxGXQNjAfI73B4sJzzqdjqIi44I0RUVFyOXdd6HX6/m///s/VqxYwapVq8jPN15uvWvXLq6//npWrFjB119/bbTv1KlTrFq1yjJ/QFd4RoLcBpQZPR8rISEhMQBMi/DicK7xl61ClQZNk44YP2fGBLlypqSa5AsKC4IQWqP9nIn0ccLd0Ya8VoezwWAgp6KOQDd7UgqrKFRpuCzej6evikcmk7E4IaBdqPxgfw6rPjhscmzH89UczKpkWnjvCsAtSQjkk0N51Dfq2J+p5Fi+Ghd7614Lz9vOlDLzxV/YmV7Wqejcgng/qhuaCfdy4nczw3vVXnfMiPLiYHbvXcLltY042lrhYm9eLEUbPi6WjdvIUdYT6e2Eh5Mti8cH8OvZil45WXemlzEv1hdHW/Nync9HJpMR4uFI0Xmu5/JaLTe8e5CMslr0egPF6gaCTWSBWwKZTMaTC+P48mghTTo9x/JUfHu8iAOZUhX4nvB1seufU/XCqucSFy2ni6sZF+xmtK1NCFk6IYgNJ4pNnSYxjDlYcrC9UJKlGS6OZ02zhlMVp0gMSGRm4EwOlhwc6iH1iq25W1mzf43JqI0ghyDKNGX8JekvzAqc1UmYtpZbc2X4lfzr6L+4JuqabvuxklsxO2h2+/2p/lOZ4jeFt0++3beBe0ahUBzH3yWofVm+l3s4PjYupFSkkFedh6+jL06qvPZ8Z0tga2XLZL/JJJcn8+6pd3lo10N8cfYLntn/DOtOrWN+6Hys5FamT3YNhJoSWL8MvrgZAkwXShvlPoqDJQcJdQ0VDsbQRNj1V+Y3NLO/4gRaXRer+TK3wde/E8XFtDVC4FbngXsYzS3NvHTkJR7Y9QCNLY1EhV7C6JjFnLGzpdjaGn8nf2zkffueOZLZU7SHZT8uk1ZktfL1ua+5O+Fu5DI5c0PmsqdoT+eD3EPA2Q82PSZeW3bOIq/ZO1pcr31hws0w88FOm09WnGTpqKWsiFvR84SIZ6SImeiK8jQInCAib9V55o3PYOgwjNaVg0YFyiwINp3rfCH2VvasSVzDrzf+ynif8UR7RHNd9HWsS1nH/834P66Lvo5/XPIPdt+4my+u/sLk3yqTyZgTPKfTc1JcV0yD7rzVoGGzRLHDlj5EGeUfFOePXwmjl0JpKsRdZX47g4jFhOdnnnmGBx54gKuvvpoVK1Zw9dVX89BDD/HMM890e97OnTtpamriq6++4vHHH+ell15q39fc3MyLL77Ihx9+yPr16/nqq6+oqBA5Te+//z5r1qyhsXGAl/HJZBA5F85uGth+JCQkJLpgeqQnh3NVbElVMPdfv3L7h0fYfa6cxEgvZDIZYwLd2J5Wxp6MCqZFeHY6f0aUF7dOD2NckBuprXm/pTVaHO2sGRvkxuZUBWMC3fjDvFHtwvWS8YFsSlHQojewPimfHGU9pwqNYycKKjUsX3uQD/bnMj2yc7+mmBHlRbSvC/Ne2c2dnxzln1vPsiDeD2UXTl+DwcDejIp2p/bHB/JYOjGIhWP9ifY1nrG2ksvY+vAcXrp+HNZW/f94mxruSWpxNd8eL+pVTnKesp6wPsRstOHtbEdFnXmRI92RU9FR6DDS2wlbaznpvcg9zlXWE+tvfh75hQR7OFCoFg6pJp2eu/8nXEdPf59KaY0WF3ubfonbPRHq5Ui0rzM/pyr4y6Y0bk0Mbb/+JbrGx8WO8po+freqzIZXYsTSZETufLmZMToSIwODwUBqUTXjgtxM7r801odzZbWUVEkFukcKdU11nFWdZZLfpAFp39fRlzJNmdE2TbOGKq15kVb9JUmRxDifcTjZODErcBaHSg4Nav99wWAwsPbUWrbnbyerKssovxnASmbFPQn3EOgUyKOTHzXZxtWRV6PT61gUscjs/v849Y9sytlEWmWa+YP3GkVpUzX+PuM6tnmEs8Alkp35O0W+s2csVJyzmOO5jTivOIrrivk241sm+03mRPkJRrmP4mTFSRZH/j975x0dVbX24WdKeiaT3nuDkBACgdC7KIg0QcCC3mvDrqBiL9d+vcLlsyIqdgFFwAuC9F4CSSAhJCG9997rzPfHhhTSkwkJOM9aWStzyj77zJzZc85vv+/vva39HU0chK9zRij8YwdMebHNzTxNPalpqMHF5LJfrttkqCrC0u8OvOQmhOW249+afBxqSiDlOGy4BYLXQXUJKOw4mXkSD1MPPEw9GGM3BolEgo9dEHF6+iRLVJ1Hk96gfHPhGwqrC1uNIf1JezY9a0PXsi1uW58dN68yj+jCaCY5Cs/g8fbjCcsN46G9D/Fj1I8tNw64C2pK4fb1fdYfgPDccIZZtT1B0woLj46tNvJjxVhg6iIsaNoj8bCI9G0+GRH8JexcITza1/gID2Tn0a28qLvDI8MeYd1N6xqzQEBMbFkbWre7zxW7jeY8f+R5fo7+uWmBkYXIrsgO77wT8ftFQccrJB8XwjPA9NfhibPCBmUAozHhediwYWzfvp3Nmzfz8ccfs3nzZrZt24a/v3+H+4WGhjJxovCHCQgIIDIysnFdQkICzs7OKJVKdHV1CQwMJCREVJp0dnbmk08+0VT3O2bMo3DmK/GnRYsWLdcYP3slqQUVvPG/i7w2ewjGenLe3RXdaDUxzFFJbE4Zb871xd2qdfrQ87cMZmGgI34OykYBNT63HA8rI1wshC+y31XiwSAbBZYKPe75OhhTA10eneLBT6db/vjH5pQxyduKo89PZXQbgnd7vDFnCE9M82TviklU1jYwd5h9uxHPMdll3LvhDM9sPs/pxALicstZcZM37y4Y2uYss4GuTGOph0Z6csZ5WPD5oXi+OpbY6fYpBT2z2biClUKP/LLeWW1czCzhSGwe1XUNJBdU4GYlbkIkEgnTBltzMKbzG3ZNRSI7mRuSdtlW5VxqEXX1KjYvF6lfL2290GeFBZuzKNCR57eE42JhxMu3+pCQV671fW6LU59BcRoA9kZScsuaxOL43LKuR64GrxMRz8nHATgen8/jv/SicIqWAUtKQSUKfR0sjfXaXK8nlzHDx4Z9UQNHJNDSPiU1JTx39DmmOE3BQN43Y7ODsQMZZS3HkjWha1h1dFWfHK89TmScYKKDePYcYTOCmMIYKuoGdqbG8YzjyKQynhz+JBIkmOu3vud6cOiDPBP4TCtR+grDrIaxY8EOLAy6lqHWHHN9c54Z8Qz/OvWvTgsNltRcNcFr4Um2TIatU7PUbzNXZkhM2Juyl9/jfsff0h/yYjQa8QygI9Vhkdci3p/wPvf53seHkz7kn37/ZOeCnQTatO3zCgjhOfWksNy0GyaiRNvAw9QDoCnd3meOSOd3Ho2TCnIqrhr/rghkycfFtntfE1YCx9eK6FSplF1Ju5jlNouPJn/ES6NfAsBY1xhbEyc+zjuFt5k3NzIppSmEZIc0vg7LCeODMx9QUF3AGLsxjV7l/Y1KrWL+H/P59dKv1DTUNF73e5P3sitpF2tC15Bdkd1JKz1jb8pepjpNRU8mfn8VugruGnwXY+zGsD5ifcvv4Lgn4c6NIsq5j6hpqCGuOA4/S7+u7WDiCHJ9+O42KLvqO9JQD4WJYOF1OeK5DeE54leoq4bdL8CxNbDn5aZ1qafg3E9w+H0hzEb+LiaEeoGB3KDbFlQjbUcSVxRHUbWwysytzCUyP5Jj6Vd5OjuNhrSzHTdWVy0KI/7xOOx9VfheJx1tsgaWSESWxgBH46FGxsbGGBu375tyNeXl5S22l8lk1NfXI5fLKS8vR6FoiroyMjKivFwYjN9yyy2trD06oqZGFMuJjo7u8j5NSNEb9x+cDj9LvGJC55sPQKqrq3t47tc3f9fzhhv33NVqdYfC4rU4796NJz3Dy0IXEz0pDpJC7vPVJTZTB3tpMdHR4mFp42Jn9OUlREe3H9Fp0lDJ3rhioqMlnIguwVxeh15dGYUVtVhIy1udz2sTTPk6pIDpXob4Kqv56nAWH8qrOJpUxk1xpZRUqzCXNVCRm0J3rTxHmUJlbirfzrdHosqnrKqOiMgodGQtP9stkcXM8DSmrqqMB787w5zBJiTEXbsq9M8GGZNRqscLezK5z9u6w888NLYQI5mkx9eFtKaci4kVRBv3rJBGdZ2Kpb+mYGUoZ4i1PnpSyEiK58pjvq9JLR+fymCabV2H36H4rEKmOkqIji5tarsH3yvd2lIuJBYSbVnDgYvFuJtA7KUYnhtjwuv7s3FW6vT5d8hTV8WdQ01Z5KdHSkIcdgo5f52OYJBl1zyrb9TxBEBWU4x+QRRVlkPx3vc6JbEnqVG681Hcp6Rk+hOt/wU1DWqe2pmBnlzCIP3SDtuT1pbjeX4TJV6LIXQbOXgQEltKQk5ph+d2o/5Wdcb1ft7HkstxVnQ83tnqVnP8YjFBZi2jnq/3c+8p1+q8VSpVt45ztugsXyV/xQSLCdxteXff9bEMYnJiGtuvV9WzK2EXALtDduNq5NrrQ3TlPY7OjsYTz8btHPUd+SvsL4aYDOn18fuKn+J/YopyCv5qfx51e5RLMS3vg7pzbUXTs893kHoQddV1/HL6F0aZtZ22nleTx1MRT/Evn3/hbSwEUmltA1k6csrrnRr7qKzWxTonBamhFEWDgpGSQBpyXiK+EFQV3etfZ+c+23g2FEN0cdfblVdW4gVkKgIo6aDtOlUdelI9qrOric6/vJ3DEgxyz6EsLyEqNYrB9U1iuv2p16gzssesOJ2UEa/gHr2MrJEvYnXhS6qNXUmNiuJQ6iHmm84nOS4ZgIzLd3E3WdyCWq1mmsG0xvO9FmNKd8eT3rI+aT2FdYW86C2izD+M/hBbfVsed36c04WnORZ7jJ/P/4yVnhX3Ot/bp33p6P2NKYuhsqaST0I/YW3IWqx0rXhryFu8Ff4Wz3s9T0RJBK8deI2VXis13q//Rf+PuXZzW/RthsEMACJNIvnoyEfc5XRXj9vv7nUVUxaDna5d4zXbJab/gOPJlyk7+j0l7nMaF+uWpeKkZ0ZCQgrGFXJM0y6Q3qwvumWpeOx6iGrT/6CWyEidtgHPnfNJsLmNBn1z3NPCqLfww+jcjyTM2oSO03yqDQfT0MXz0eR3ysfYh1/P/soky0nsz91PoKnwhz574SzGcqF/mkpsMIg5SpayfXHcNGE7CoUHGWPexGPXEioyYsEqkMyscsjS3Hezr8cTjQnPzz77bLvrVq9e3e46Y2NjKiqaZplVKhVyubzNdRUVFS2E6O6gpydmhHx8fHq0P6pBcPgxfFxshJH3dUZ0dHTPz/065u963nDjnvuLv0cwwtmMxaOc2lzf3fPuyQDb6/GkB6y1csbUUAdTQ5EqtC+gi7PKzbB1ruXD44dwdPMk70IUQYNs8LRW8OnpfG4ZNQRP69aThiObZU1tsnPh/u/OYmUgI6ZEjqGujDFDLPDxafuz6A7mxplYOblhp2wZaRVz4jT/GDeYGUNsUKnUSCRc82Iqg9VqZPtzKa6XM6KDz7wsLJSZPnb4+PRs1nlQQRJphZU9vq5Oxufja1/Mf5cEMH31EUa4mLVoa/BgNV+EHqHKyJZAl/Z/xwp+z2B8wOAWn0VPxpOUhmy2hKbh4+PDl+HnmORn2Xit7PDzoaZehdKg730KhzdLvBoZWUt4sZzEahkrZ3QeNXSjjicAhP0IwW+I6ufOYzHNOQXZJ9k0+nfmhN6PkaMZa8+U4WRlQkxWWef92/MKDJmL+eiHYcsDmPv4sDs9lsKqfNw8vdHXadtL80b9reqM6/28j+Ul4Oti1OE51CmK2bclotU21/u595RrMZ4ASKXSbh3n9R2v8/bEt5niNKVHx+sq1lXWrE5c3di3A6kH8LbwFoWxio8ya2T3LSCupivvcXVcNcMHDWewuRAEA0sDKTcuvybXZEVdBaU1pe1GJbdH0sUkXgp4CVelK8Nonc5+rb5Td8nv4mTmSe71aVvwi4mLwdbIlvVp69kydwsGcgNii2JRXzQhKGBi0/2bQSFk7Wfbwu3oy/SRlGaCvoJBAWO63ac+OXeVNxyywX7KP7Hv5Ll/n8c+zPRb1lfB0Qz7iDdJMVTh4+EsUuBVDbA9WBQpdJuA+5g5oHobu6CHQVaMsaoeS1dLDC4YMNa/dXRlW+d4LcaU7o4nvaFB1UBYRBiGckN8fHxE0e5zqXx+6+eY65sjT5bzQ9QPJJUnYVJnwjTDacxwmdFhmwnFCUQVRDHHY06H27VFR+/v9uDt3OFzB/M85lGrquWJA0/wc/7PBNgEMG/UPGbUzWDW1lno2+vjpnTr9rE7IjM8k1sCbmmzQOijto/yzKFneNvn7R63393r6nTkacY4j+n+dVI2C0VRMvbN94tJBPuhoi2zeoj7vmW7J/bCsDvRL8+BMY8zyCsI4mfjXXMBfJdBVR56/9gO4RvxGN393xRNjidLDJbw6blPWRC4gAtpF1jsvxiDBAMKFAWMcr08eWdSCf/7E9O2jtlQJzIJY76FRRsY5Doaql9C+ddL8PgZlJaeGunnFfp6PNGY1cbMmTOJjo5myZIlrf46YsSIERw9KvxPzp8/j7d304Ogh4cHKSkpFBcXU1tbS0hICMOHD9dUl7uHVAo2fqIiqBYtWvqNY3H5fPBXDCVVdf3dlWuKq6VRo+jcU8yMdJnoZclXx5LYF5XNbH973KyMUBro4G7ZuS/UIFsFx1+YyopxVpxNLuRSTlkrn+WeYmXc2maisrae86nFjUUEpVJJv1TwlkgkjHa34EJ2x161yfmVuPbSaiOvi0UW2+JMciGj3MxxsTBi7jB7PK6yXZFIJCwe6cTms2nttlFd10BpVR3Wiq5FBHeEk7kBaZeLC17IKMHXwaRxnb6O7JqIzlfj76jk5+BU1h1OaPQN/9tSEA/11SLVN+BuuOlNuPU/6Nl6k6HjAnnR7L2Yw9PTvSmrqe/YoiQrAiI2w4x/gc1QqCyAknRySsR3Jr1I6/N7o5FdUoOtsuNxwttGQXJBhdbeZgCjVqtJLU3tM1/n5pjrm9OgaqC4upg9yXtYG7qWuR5zme48nfO55/v8+FcoqCrAQr/JbsLXwpeL+RevybG3x2/nneB3urVPbmUulfWVTT7C/cjNrjdzKvNUazuNy5zJPsMDQx/A3tieExknqKyr5Pkjz/NC0Ist79/MXKEoCQO5gViefwksB5CFhFQGK2O6FGzWSnQGMLbBor6O/Es7YNNdwmIjLwaMrGDZNvJHP8z3UT/A+KdARx+mvQbTXiWlNOVv6+EMEJYbhqWBJXlVeVTXV5NWloaRrlGjtYyPuQ8ReRFMdZ7Kfyb9h/eC36OkpoS6hjpeO/EaB1IPtGpzb8pe3g1+l9LajrO2usrupN3cs+sediftZqbrTOyM7XAxceFe33vZnbyb+/3uB8BQx5Alg5bw/cXvNXLcK5TWllLbUNtiDGuOp6kn5bXlfWbz0Rbhed3wd26O7VBRFK85ec3GAlMXUVywuYdzzJ/gtwiWbQOvm8SygDshfCPkRAlLEQsPmPZqj85Fk9ziegu3ut3KjC0zqKyvZKLDRCY4TOBkRrOCtta+UBAH9c2e/6L+gJRTcGItRP0PFn3bZKsx8gF4YC9oWHS+FmhMeJ4xYwbjx4+noKCAoKCgFn+d7aerq8vSpUt5//33eemll9ixYwebN29GR0eHF198kQceeIClS5eycOFCbGxsNNXl7tPWl0OLFi3XjMKKWkqr6pjhY8M3XfDc1dKaZWNc+fhAHLP97bFS6OFgasDh56YglXZN0JVIJFgby9HXkRGdVYqXTe+L0AFYKvRa+TyfTizAz0GJsV7fFaDrKkFu5lzIaV88U6vVpBRU4GLe88IOVsZ65JX1XHg+m1xIkKu4OX93wVBenNnaK3HhCEf2XMxp8V5X1zVwIj4fgKySamyUesi6eD10hJO5IelFlZTX1JNRXIW3hq6V3nBHoBP7V0zGUE9GYWXv/LSvewoThPefWgXet8CIe2HoIqwV+iRKnChLjyS9qJIRzqbYK/U7LhIX9r2oh2FkKSbqvWZAzC6yS6uRSSWkXy4yqeXGIbu0ChuTjoVnfR0ZbpbGxOZ0XtRUS/9QUF2AnkwPE12TzjfuJRKJBBcTF8Jyw3jz5Js8M+IZ5nvOx8bIhtzKXNTqvp8MbFA1UFpT2kIsHGIxhKjCHhTN6wEJxQlE5ke2Otd6VT2ns043vs6uyG4UwyPyIvC39O+XiferMdE1Yaz92DYFPrVaTXBWMGNsxzDefjzBWcHsSNiBs4kz8zzntdxYYQdVRcK7FISnq7n7NTiDbiDthUwikWBlZEu+ug7KsuHSLkgLFn6u9sPZXpnEmtA15FeJey/0jMHAjNTSVJxN/r7C84HUA8x0nYmTwonk0mSiCqIYYt5kgeOocMRYx5h5HvMYajWUm11u5tkjz3L/nvuJK4rj/8L+r5X/c0JxAnoyPdaHr2db3DZqG3p+75dflc8HZz5grsdc7vW9F1ela+O6uR5zeW3Mawy3bgqSvHPwnexP3U96WdftYTsjrSwNR4Vju+OBVCJlpO1IQnJC2lyvadRqNedzzxNgHdD9nW38IDsSVKqmZc2LjOqbgKGFKFwNUJ4LuTHgNrFlO26TRQHF4/8Vet0A4tGARzmx9AQ/3foTxrrGjUVOG9HRF2Nf7uXfIFUD2X+tomrTXXDqc1i0AVzHN20vk4PjyGt7EhpCY8IzwCuvvG2ObEQAACAASURBVMKsWd0LaZdKpbz11lts2rSJzZs34+HhwZw5cxojpadNm8bvv//O1q1bufvuu1vs6+joyK+//qqx/neKnb9WeNaipR+JzChhiL0J8wLsOZFQ0N/duS4Z427Obf52LJ/UdINvZtT9SOogN3NsTfQ1JgpbGuu2ivY9cimPyYOsNNJ+bxnjZk5kTvsRz/nltejKpSgNex7Fa6XQJb+HwnNdg4rzqcWMvGyhYaAra7MvVgo9Zvvb8f3J5MZlW8MyeOTHUFQqNRlFVTiYaqawlIm+DnKZlBPx+QyyUaAj0+gtR48w0JXhbGGIrYk+2SUdR7Df8BQkgO8CeO5Si6gua4UeMQ325CVEMM7DErlMir2pARkdCc85F8Gh2Y2w7+1wcSs5pdX42ZuQpo14vuHILqnuNOIZwM/ehMgMzUSaadE8aWVpOJn03i6rq7iYuPBL9C+MtR/LdJfpSCVSDOQG6Mv1Ka4p7vPjF9UUodBVIJc23bu4Kd3Ircwlvyqfstq+nSRJLEmksLqQzIrMFstPZp7kob0PcbHgIrmVufzzr3/ywrEXUKvVPY8m7CPG2Y/jbHbrYlhJJUnIpXIcFY4E2QVxJvsMe1L2sMBzQetGpFJRDKvksiBXlCyioG8gLIMeJc/YEmZ+ALtWwaXd4DwaEFGzbiZu7Erc1WKflLKUARHZ3l/EF8Xja+GLh6lHo0XGEIsm4VkqkbLptk2MshU2BU8Of5JRNqNYPGgxP976IwpdBftS97VoM6E4gVWjVrE7aTefh3/O7qTdPepbTkUOTx96mgWeC1g8aDEPDn2wxXo9mR6LBy1uIQib6Ztxt8/dfBz2cY+O2RZpZWmdRsUH2gS2KNDYl2SUZyCVSLEz6p59ECDuPfWVUJwsXpdmQfw+URTwCq4TIemw+D/2L/CcBvKrihpLZTB7DVz6c8AJzyCKg17B09ST/Kr8xqKDANj6i8xB4EzYehaY6/FGwE1w+1ei6OgNgsaeAqOiOp4pvnjx2qQw9SnaiGctWvqVyMwShjooGeqoJCqzlLoGVec7aWmBRCLh07tG4NoFa42OGO1mjret5iJYrYxbRzwfjctnktfAEJ49rY2prFO1G/WZXFDR6/fUyli/x1YbkRklOJkbdkn4fniiOz+dTqGiRlSm33w2ler6BmJzy8gorsTBtOd2IVfjaGbAR3suMd6z7Yrw/YWNiT45pTeo8JwfD6GdpHaqVFCYJKIsDFqmCVsr9AmrtKYqI5JJ3uL7Z29q0HTtR/4u/q6gVov0RhvfpmUe0yAvBlVJBoEu5qQXaiOebzSyS6qx7STiGcDX3oQLGe0XvdXSv6SWpl7T1H4XExeCs4OZ6NAyYs3GyIacypw+P35BVQEWBi1T1OVSOYPMBjFn2xzu+vOudm0kNEFicSL+lv5cyBfPk9X11ajUKnYn7cbXwpd3T7/LvbvvZaH3QuQSOSE5IYRkhzDMeuAIzyOsR7SM2EOcxxsn32CB1wIkEgmDzAaRX5VPTGEM4x3Gt92Q0glKLlt/FSWDuWZ9cPsbK99FFNSWoHafQobvHB4qO8duWT0X8i5QXFPMqqBV7Ezc2WKftNK0v3XEc2ZFJnbGdrgr3dsUnkGMIVfEXWNdY5YPW84cjznoSHVY7r+c9RHrUanF82FdQx1pZWnc5HITBxYf4LUxr/FLzC/dzq44lXmKpX8uZbLjZJ4a8VS39r1vyH2E5ISQWNI6U1etVne7L+ll6TgpOhYjR9mO4lTmKTLKMzrcThOczxPRzj3OyGiur+15GQL/IawyruA2CZKELS8xf8Kg2W234zFVWMZ53dyzflwjZFIZQy2HEpHXzL7XPgDSz1DbUMvKi+v5t9Nsoiuz2atzY+kcGhOejx07xrPPPsu+fftITU2loqKC7Oxs9uzZw9NPP93o43xdYzVYpALVaSN3tGi5luSV1bBm7yVCkovwc1Ci0NfBydyAmCxt+m5/sTDQkdV3aO5ByPIqj+fUgkrKqusZYtf36b9dQSKRMNRGn+CktiPtk/MrcLXonfBsYiCnpk7VIz/UE/H5jPPomrjramlEoIsZf0ZkEZNdSm5ZDbf52xOaUiQins00E/EM4G5ljIuFISu6UMjvWmJjokd2O8JzSHIhSfkVba7rd1QqWDcBcjso6BG7G47+p6Un3tWUZoCBqUjvvQqloQ5PLpmDhySDWb7C3szB1ICM4svv1+kvYMczIjKlshBKM1DL9YTNxhXkutR738rd9dsZ5mjSdx7PIRvg8L/7pu2/GwkHoaG+S5uqVGryyms6tdoACHKz4HSiNkNpoJJadu2FZ6CVGGljaENOxTUSntvwRn1qxFOsm7GOqU5Tefn4y31y7MLqQurV9Ux2mkxkXiTlteXc+eedPHPoGY6kH2Ht1LUodBU8PeJpHhz6IAu9F/Ls4WeRSqQt0vf7G1elK+V15eRW5gLCvuTl4y9jZ2THcv/lgBBXRtmOYrLjZPRkem03dLXwfINFPBvqiEn89PJ07i87h4/LdH5I388j+x/hzsF3Mtp2NLmVuS1sGFLK/r4ezyq1ipyKHOyM7HA3dWdvyl7iiuPwt/LvfOfLTHSYiFwi53DaYQBSSlOwNbJtvAbH24+ntKaUsNywFvtV11c3WnTUq+rZFLOJ2KJYQGQprDq6ig8mfsDD/g8jlXRPPjPUMWSK0xSOpx9vsVytVrPi8Aq2xG3pcP/l+5bzZ+Kfja9TS1M7zVLxNPVkpttMluxcwpMHniSxuO/sKcNze5mRYecPGWHCkibhAEx6vuV6t4mQdAxqyiD5hLBya48JK1qK1gOUAOsAzuc1q2swZB5E7+R09Bbca6uZNOY5Vgau5JeYX/qvk32AxoTn5cuX8/zzzxMZGcmqVau4/fbbefbZZ4mJieGFF17g0Ucf1dSh+g+5Hlh4dvzAp0WLFo3z8YE4fg/L4GBMLr72Qogc7mTG+bSiTvbU0lfoyKRYGLfzMNEDri6sdyQuj0lell32nr4WDLU14ExSYePrS9llLPsmmHs3nCE4qRCXzgoLqtWw63lRMKK6RAhmWx9u9DiUSCTCcqQHdhvH4vKZ6NX1qOI7g5z5OTiFd3ZGs2ysC6NczQlNKSKpoBJHDVltALx/+1DW3RM4IGw2mmNrok9Oadvv83/3x7LtXN9HifQIqRTGPQU/LYTyPLGsogC+uaVJaM4KFw/zRcltt3HiY4jfL+5n2mGUrzf6OnLM1CL13eFKxHNFPuTFwsh/wicjYPUgiN9Puq4bq7aEcym7jMVfnqK+QUXWyFWMlscyOulz0vrK4znsBzi+psn/T0vPSDkFPy6AlBNd2jy/ogYTfR105VJx3Z36DIK/FA+GV+Fjp6C8pp6UggE6mfM3J6302lpt+Fj4MMFhAtaG1i2W2xheo4jn6gLMDVoXjBtlO4phVsN4yP8hzmaf7RO/6cTiRDyUHgy1HMrh9MM8sv8RhlkNo6q+iiHmQ7A1suXLGV8yy03YVs71mMs052msm7GuffG2H5BKpARYBXAu9xzV9dW8E/wORdVFvDPhnRai3FMjnuKp4R1Eh5o6QXGaGEOKUm444RnAysCK32J/w9fSl5WzvmTjbZs4cecJHhz6IDKpjNF2ownOCuZU5il+vfQr6WXpf1urjfyqfIx1jdGX6zPIbBC5lbmsnboWpZ6yy21IJBKW+y/nmwvfABBfEo+nadO9jkwqY+XIlaw6soqDqQc5mi4CI58/+jzL9y8nqiCKx/Y/xs7EnTy09yEO5h3kt0u/cYf3HYy2G93jcxtjN6aFhzuIQqOH0w53WNg0ozyDyPxIPjz7YaO9TVpZWqcRz1KJlBWBK9i7cC8+Fj68dvK1Tse08tpyKuu6d6+mVqs5nXWaQJvAbu3XAo9p4p408bCIbta56hlE6Sgy87Y9Ag4jRNDEdU6AVQAHUw+yO2k3dao6YTvkPpkDJ97lJqsRYGTBaLvRRBdEU15b3t/d1RgafRK0tbVlxYoVbNq0iT179vDzzz/z9NNPY29vr8nD9C+2Wp9nLVquJTHZpeyMyGTHkxP47ZGxeFqLCL0AZ1POpfW9H+CAQqWC2hszZd3dyohL2U0+oLsisrhpSD8Vk60ph+TjrRYPtdEnOFEIz3UNKlb+ep4x7hYEOCrZEpreecTzhd9EpeLtj8KWByAnUlQ5j9vTuIlVG0UWO6Oipp7IjBJGu3deff0KUwZZk1dWQ229iocnujPS1Yx9UTkEJxYwRYO+2sZ6cuQDTHQGsFHqk9OGx3NtvYrQlCIScgfwjZ7/Yhg0C059Kl5f2gVpp6EoSbzOCgfrIW1ew2RHwsF3YNdzHUeFSCRg6wdZIiLD3kQXg9xzELsH3CfBtNdg+VFRkPDA28TizI7wLG7//AQX0ktIyq8gs07B5+YvYJX6J2l9YbVRminE9YnPivNpqNP8Mf4ONNTDn8+KdNfkY13aJbukuinaOeQbOPczRPwqItCvQiKRMMnLiqOxeZrstRYNca0jLN2V7nxx0xetlnckPJfVlrUqGNZVfor6ifO558mrzOOnqJ/ajXi+gkJXgYHcoKnomwZJLEnE3dSdYVbDmOgwkQWeC3hlzCt8Nv0zVk9Z3Wp7pZ6SN8e9iUK3/wvzXs1w6+FsiNzA3O1zKakp4f+m/R+6spb1QtyV7tgZd+D7eiXiuapI/OZcZft0I2BpYMmfiX8ywWFCm+vH2I0hOCuYdeHr+CjkIwzlhhjp9C577nolszwTeyOhGbkp3Tiy5EiPIv0nO00mqSSJgqoCEooT8DBtea8zw2UGTwx/gu8vfs/Lx1/mZMZJwnLCsDG04bH9jxFoE8h3M79jwy0b+DntZ3Yk7mCh98Jendtou9GE5YZR11BHvaqeV46/wtqwtTw/6vk2LTiucDjtMNOcp/Hm2Dd5+/Tb1KnquuTxfAVDHUOW+y+nqr6Kg2kHW62/UhC0rLaM+/fcz4rDK7o16RZdGE2dqg5fC9/ON24Px1FQlgVhPwoRui3mfSYKDk5+oefHGUCMsh3FbPfZbIrZxMN7H6aouoj6UQ9ySFfC9AmvAOKz87fyJzg7mLSyNO7fcz//PvNvIVRfp2imKtTfCa3PsxYt14yXtkbwV2Q2L84ajLmRLuZGTcJagJMpXx3ru9ShAYNKBelnwH4E7HlJiEoP7BM36TcQvvZKckpryC2rpqZORUx2KdN9rDvfsS8I/Q72viJucKY2pdy6mumSX15DfnkNO8MzsTTW47Ep4obWSqHHOM/2H2apKhbeZXf9Cqc/FzYFd26EiM0QvlmkWSEsR7ob8XwmqRA/ByWGul3/SZdJJXx+TyCOZgbIZVI8rYyxVxrw9nw/rLuQPn+9Y2uiz97S1iLHhYwSZBIJ8QNZeAYR9bx+MkxcCTE7QccIMs+BkbUo1nTz20JEHLGs5X7HVsO0V4S/s1Mn0TsuE0Qb9iMYeehO/PLiYGcN3LYGZDpkyh35Ln8iL1d+zQWpA/9dMgy1GnZEZBKVVYpEIkFt5oE0OQczeR2RGSX4OSiprmsgNqcMf8fOo1bic8t4ZVskUomEDxYOxeXK5E7sHjFx4zkDxj8NGaHw/Ryoq4TZ/wXHXkTf/M0oijlEQUE1GSNfYFLSN3TllyW7pBo7pb4Q/w++K36Tko+Kz6ENJnlbsiM8i2VjXTXady29pzsiRl9iY2RDWE5Ym+tWHF7BfM/53OZ+W7faLK0t5ZNzn2BtaI2r0pXj6cdZ6L0QWyPbDvdzVjiTWpaKlaFma0zEF8fjrnTHUMeQF4JaCihXi7YDnZtdb6airoJpztPws/TrWSOmThCRLiZNb8BoZxDCc1huGGPtxra5frTdaD448wH6cn1+uvWnNos2/l3IqshqMVFhIO9Z9p1cKifQJpCzOWc5m32Wf/j+o9U2C7wWsMBrARsiN7Di8Apmu8/m9bGvo1arG72KPUw9mGM7h2xJNg7GDj3qyxWUekpcTVwJzwunTlVHbFEsu27fRVV9FevC17W73+G0wywdvJQpTlPYdGkTKw+tpLS2FBvDrgfmyKQynhr+FJ+d/4xpTtOQSCTkVuYilUjJqczhkf2PoCPVYYrjFJJLk9kWvw0ffLrU9q7EXdzqdmvP/Z1BFAb0uhnCN8L8z9rexmWs+LtB0JHp8LD/wzzg9wDvn3mfN06+QZBtEB62gThYNnmaT3CYwA8XfyC5NJn7fO/jTPYZHtn3CB9N/ggz/etvom7ghSENdLTCsxYt14S/IrMJTizk5IvTWTKq9UORp7UxmcVVVNZ2zZPyukOthugdsG48/HqvEJli9whbhotb+7t3GkcmlTDazZyT8QVsO5fBbf726MllLTdqqIczX8EfT0BuTN91JnwTLPpWzL43G++lEgnDnc0ISyni0KU87gxyRiKRIJFIWDbWFWtFB4Lt6c+FSOYwAuavg3u3g0wHfOaKyNTzG6EwqZXlSFe4mFnCcOfu34AEOJliedkuRSqVsGfFJILcuh413S+c3wiZ5zvfrhPaKy54JqmQOcPsSS6ooH4gFy81c4FBt8L2x4Tn3aj7hfCcEwnWPuA+FeIPtLxfKc8T/nkj74c5ayHgzo6P4TpBXJtnvkTH2pux9V+gWvAlDJlPeU099393lq9jdCgd+SR7KrwZ6WrOrKF2DLEzISqzlJySaqyURkgsvXhwcC2/hggvz99C05n32QnWHenYHiM6q5Sl64O5zd8OuUzC+SsZLiUZsHkZHHoffOYIG7TFP4LfQjD3gLi9vXlnb2zqqsSkVzOKwndzRjeI18MUqLIuiIyPDjgRn09KQSU2Sn3xoDhkLlh6gqU35Me1uc9YDwvOJhe2uU5L//LBxA+6lcreV9gY2jR6BjentqGW87nnCc1pe1KjI3Yk7GCy42RcTVxJLklmiMUQDqYe7DDiGcDZxJnU0tRuH68j1Go1R9OPEmQbpNF2+wsnhRNPjXiq56IziIjn4lQxEXqDCs9Whla4mri2G/ltb2yPhYEFc9zn4G3mzd0+d1/jHg4cMsozGiOee0uQXRDb47aTWJzIePt2ilsCSwctxUzfrDGi+WoBdb79fP479b8a6dMUpynsTNzJgdQDzHKbhZGOERb6FjSoGyiqbm0dmVySTGR+JGPtxiKRSHhl9CsMthjMD7N+QCaVtXGE9pnkOInKukrC88IB+OjsR7xw9AW2xW0TBRlnrOet8W/xzoR3+L+w/+NQ3qFO26ysq2R30u5uTwi2ifdMUez6Bh0H2kMmlbFq1CrSytL4/PznvDX+nRbrJzlOIrUslY8mf8T9fvfz2bTP8LX05d7d91Jdf/0VSNeo8Fxc3JT2XlBQQGHhDXiTaesHORdFFKIWLVr6jLd3RvHe7UMx0G37x1VHJsXT2phL2TdggcH6WthyPxz+QFToXRkDAXfBog0w8z3Y/68uF4G6npjgZcnvYen8eDqFJaPa8C/b8zJEbgVTZ/juVri0W/OdyL4A1cUwZL6wEQj9vsXqEc5mnEkqJCylqH2RtjBRCHZX0tVyo+HMepi8SryWyYXoDKBvAhNXQNR2+GkhdobqFkUWu0JeWQ3WioHj/9hnnPlKRKL//mCjL3ZPsVXqtyou2KBSczQ2j8neVlib6JHaF/YQmmT2GtBXgvtkcJ8iBPmscLAbJmw0ZvxLRAHniQI5JB0RUcx6XUzbdhwphMSwH5CNewJDPT3yXWbRoGPEUxvPMdzZlPGelhxyfJSUBgssjETEnq+9kouZpQQnFTDYTgE2vsyyLuR/4ZlU1zWwIzyTt+b58f3JZEJT2r5PLKmq45GfQnl1tg/Lxroy3qyYzILLVjwpl4vLrEoUwjOAXBeCHoJhSyH1VM/f0xsNtRp+vgMifoPdL8KH7vDFWKhvmtxSpB1G4jWDib5O5BoNgti/2m0uMqOEezec4T97L2Gr0IPzv0DAZaHEchDkXWqzqKWVsR619SrKa268363rnUmOk3oXraYhrA2t27TauFhwER2pDudzuzfhqFKr2BSzicWDFvPexPf46uavmOAwgbyqPCwNOq6H4KRwIq0srVvH64yI/Ah0ZboMNh+s0Xava0wcRIp93qUbVnByNHZkouPEDrd5efTL3Od73zXq0cAlqzyrY2uWbhBkG8SJzBPc4noLOlfut9vAUMeQPxf82aFVRHeLCbbHkkFL2Jeyjz3Je5juPB0QQreb0q2V3UZZbRlPHnyS50Y+11ik0tnEmccDHu/RGCKVSFk8aDEbYzZSp6rjROYJMsoz2Ba3jfme8wmwDkBfro+3mTffzfyOX9J+4VLhJepV9RxIPcCuxF0t2mtQNfDCsRcY7zAed1P3Hr4jzfCZA/f+0ft2rkN0ZbqsnrKaNVPXtKq34KZ04+AdBxllOwq47FEeuBIvMy++jfy2P7rbKzQmPJ85c4YFCxZQUlICQExMDAsXLiQkJERThxgYGJiBoVmTl6IWLVo0Tll1HYUVtYzuJPpyiJ0JUVmlHW5zXfLHY1BfDQ8eAO9bLhcUexKcgkThBRMHIVRqmkt/9Y2Y20XGeVhSHH+Gt4aX40dCU/E0EN7WF36FhV8LAffu30Tk8xVRrbuU5wpf0qtTw89vBP8l4j0ffg9Ebmnhqx3oYoZF6H95S+9HzKvbeDA99B58czP870nYfA/8skSIf9NeBXO3tvsyYQXctRnsA7gp+2vyyqvZmbiTvMqueaLml9dieaMLz/W1YsLlwf1gPVgU1zv5SY+bMzfUpbKmgeq6BuJzy3lnZxS3rD1Kg1rNBC9LPK2MB77dho4+zP8clvwkrHgyz4vIeq9bxPrh94go+/Qz4nXSETF+dBW5HjgEivHG1g8HMwMyi6v59kQSVbUNvDXPj8G2CvZH5+JsbtgoXg2xN+FcahEhKUXc5m8H1j6YlcUxzsOCNzYfJyc7k8UjHXl6uher97b9/d1wPIkgV3PmDxfprYuS/4Vd0uXK78nHwHWimLS5WjBzChLV0fvT71mlgm9vFRXa+5vcaDEZcegdMSG2Mkr4f8cfEOtLMtCvycN60FjGuFvws8Gd8NeLQlC+CrVazVs7onhzri+fOh7gvkuXi4Y7igcijC6LeRWtvXElEgn2pvpkFVf1xVlquQGwMWo74jk0J5Q5HnPILM+kpKaky+3tS9mHQldBoE0gCl0Ftka2BNmJaGMLg04inhXOpJSmAPBn4p+sj1jfjTNpm91Ju5nlNmtAiPwDBh198Vwd/CX43t7fvekT7va5m2cDn+1wmwkOEzRu63I9klmRqbGIZy8zL+yN7JnnOa/TbbsbPdxTzPTNmOsxF0sDyxYFJN2V7iSVtNSVdiftxtPUs9fe0s2Z7zmfk5kn2Ra3DUeFI6+PfZ05HnNaWQ+5Kd1YYL+ANaFreHjfw3xx/gtWh6xu9H5uUDXwxsk3qKqv4rUxr2mmc1KZCCr6m+KudGeM3Zg217X1m7Fq1Cp+ifmF7IoBcJ/ZDTQmPK9du5Yff/wRpVKka40fP54NGzawZs0aTR1i4OA4SjzAadGipU9IL6rC0cyg0xv0KyndNxSRW4VQsGiDuClvi/FPw7E1cHxtz4XXtjj+X9j6sGi7H/A0KOcPw7eZlfkp7HgaPg0Uwu0V2xGHkaC87LPmECh8kTuIzmsXtRq+nSWKsv28WBT8AyFWXfgNhl22IDB1EnYDJ/6vcddhlg3co9qBvYkO7FzRst2wHyDyd3j0lPizGix8y565AKMe7LxfM/+Nd+Z2copDeeX4KxxJPyIigQ691+FueeU1WBp30x8y52KbkYkDlvSzIp3f3B3mfQ6B/4DT69r1lO0MqVSCrVKfR38KZfGXpzDUlfHOfD82PzwGhb4OntbGxOcNcOH5ChIJGJqDsRX4LoBBM5vW2fqJgoIAiUdEdHR3GL1cTJoA9koDMourOJdWzNIgJ3RkUrxtFByOycXRzLBxF2uFHvo6MuYNsxe+49a+kHuRDxb6MynlU7bovIFeTRELAx3JLK7iQnZrMTImu5RJ3pcfxNVqTCuTGZK3m7LqOipjj4BbOxFkBmbi4SU7onvnqUnyYkRU9oXf+q8PV4jbIyKJnggVk1sGZuB3uxinAHXEZo6qhjHUyZwgN3N+zHGjYdH3cKq1z+Lx+HyKKmu5K9COm0u3ohz3T5j/RZP4L5Fcttto+zfJ3tSADK3wrKUdFDoK1KhbpZyH5IQw2m40fpZ+jWninaFSq1gXvo5Hhz3a4j7S39IfYx1jrA07rh/hbOJMWlkaarWabyO/7Xa0dXPUajVfhn/JjoQd3OamgZT0Gw23ycL6yT6gv3vSJ0gkkmsmbF7vZJVnYW+sGeFZKpGyY8GO3lnB9AFPDH+C1ZNbFhJtK+I5NCeUSY7dCBToAko9JY8Me4R3g99lkuMkxtmP481xb7a57QzrGaSUpuBi4sLm2zYjkUhILRP2Q1+Ef0FGeQYfT/24w2hyLX2HrZEts91ns/nS5v7uSrfQmPAsk8lwdHRssczNzQ2p9Aa0kR56h/AA1aKlq8Tvh01399oe4cXfIzRbGf7Q+yIiaoCRVliJo1nnRSWG2CtvrIjnmjLY/YJ4mNfp4Py9bgZLL4j+H4R93/523aG2UthMLD8ioiazuvaAp1FyIpE6BYmo1keOwfOJImrw+Bo4sVZEcDbHfTIkHe3+cdLOABIRKbpsG+x6Hi5sEVGAZq5C4LzCrA8h5BsRRQko4ndyTnckBRPeFKJwzkWxXWWhiMhd/KMQAHX0YfprMOqBjj/L5hhbUeQyk+T6L/Ex9yGhMBZ+f0AI3x0IrPnlNVgZdxDxXH+VdUdxKnwxvm+i5vuKxMPCTgJEpKv/HTDuCTH50kN+uD+I+cMd2ProOFbePIgx7haNIoWXtYKYrOvMxueBfTDt9ZbLbPyE73NRsvD3tepmiubg2eApUkLtTYXwnJRXgZulKPLnbaOgrKYeZ/Mm4VkikfDoFA8emHA5/dJmCORcxERPziyDaJQeI+GPx9GRbeYQSQAAIABJREFUSXlwojvbokQUo1qtZsPxJNRqNQl5FXhaG4v9y7JArotVbTrRB3+htrwQtdVgqmob2q6+7jwGUk937zw1Seop4TUdrrkHgrd3RpF41UTIwZgcUgs6sYOJ2yci4GXyJoHYZ55YHrML1YmP+UZnKdYm+lgr9LEw1iVGZzCUpEFZS9uDb08k8+BEN2RJh8HCU4zHTlf51Vq1Lzw7mIqIeS1a2kIikRBgHdCiwGBeZR4ReRGMsB5BgHVAlwXg0JxQpBIpExwmtFiuI9Nh1+27umS1kVqWSnheOCmlKW1agHSV6MJotsZtZdNtm1qlUWsBFn4lJsO0/K1pUDWQUZ6Bo8Kx8427yEAs2GmkY9TKmsJD6UF8UXzja7VaTUhOCIE2mi+SvHTQUma4zGCm68wOt9OR6rBt3jZeH/M6MqmMkbYjCckOIb8qn40xG3lvwnuNFiBa+oe7Bt/F77G/E1UQpfGaBH2FxlRhtVqN6irf44aGBurq+jHdsa/wvEkUQsiP73xbLVrqa2HXKiFSHX6/V00di8vn9T8iqalv6H2/Gurh9BdwceCJT2lFVTiZd/6DNthOwaXsMhpU11HkJgh/zdg9rZef/kKIa44jO95fKoXF38PMfwtBThOknwUbXxFRetOb8L+nQKWB66w75ESJPlxBJheRMEdXw9BFohBfc1wnCoHpamG1MyI2CS9YiQTs/GHZdtj3hvAOvrrgmok9TH8D9r4qIoQjfsV31kPcPNRJRDH/9aLwgd76kHh4shnS9jG7yPnB09FVl/CYoScJMdvA1AVmvAVH/tPuPvllNY1FAltRngerB0FxM1uQ0O/AaTTse73XXsmAeF/ObxQTJ31F4mFRMK85I+6FlJNQ0HGRuvZwtTRiXoADrpdF1OZM8rbi8KVcquuvo3oORpZibGiO7VAR/Ru9Q9y79CLN295Un/SiKpILKhrfMy8bIQ47m7ecXHlwojvOFpfHcIUdGFnD2a+RokJ3/ifCA722kttHOHAxt5qUggqis8p4a2cUsTnlpBZWNorb5MeC1WB2NIxjaOgrfFS3iEu5FSz84iQ/B7dxs23nD7lRPT7PXpN6GsY/BVVFTdHmvaCsuo7vTyZzz9fBvLY9ku3nMsgtrebJX84x59PjYjK6plx4OO97o8m/uaoYsiJaR4cbW8GMf1G45Rk+qbkNc8emyYggV3PC0srE2NrstyUtJpRhKd8xz0cB534QARhAcXUxOxJ2NLVt6S3uddrgysSFFi3tMdp2NKezmiaNPjjzQWPxr+HWwzmXe65L7RxLP8ZUp6ltZs2Z6XdeiFepp8RAbsDjBx5n2ZBlvUpnji+OJ8A6ACeFVnTWoqU90svTMdc3x0in9f3YjY6vpS8XCy42TqRnlGegVqv7ZMyQSWV8NPkjPEw9Ot3WQN6UeTzSZiRnc87yxfkvmO0+W2Ne3Fp6jrOJM4E2gTxx4AkeP/A49aqBX0NDY8Lz3LlzWblyJTExMZSXlxMfH8/zzz/PrFmzNHWIfkGtVpNdkd2ycqRMR9x0R1xf4e1a+oG0MyJi0dwd/rlLCIudVIxvj7oGFXllNbhZGvH9yeTu7dxWMczscKivgoSDPepPX5JeVImTWefCs4m+Ds7mhpxNvs4KmcbthY1LoSSjaVlFvrg+przY9XbshwtBsVwDUfDJx8H1cuXngLtFAbLgL5v1r0DYUjQrStUmqgY4+7Xw373ijVxXLUSRcz8Le4ScdkSh3Mv+o82xHw4vpsDEZ1uLaobmwjc5M4xWRP4u7DquprZSTLb4L25aZjMEng6HuzY1FcpqzrA7oSwbu+C3oLIAi2G3IpdJYcwjQqBJOSksOaa/3nrfDlCr1bwX/F6L1OKj1THIy4bhmZ9Mgo4M7vgORtwHacFQki42yrsEG+8CoKa+gaq6BpQG7aS7Ba8TxRLj90HaWTjwFoT9CHM/EcXALqfc9xi1Gva9JoT5b29t09+111QViWvDaXTL5bpGIury7DdQlCI+B4DoneJ67kWGia1Sn+HOZpxMrehFxwcAxtYg04MTH0Ng74oXOZgacC61CENdOSb64noz1JXjbG7Y8UShRAJjHxPXiMdUMbbYDYOUkxjqyrnFS8F3J5M5HCv8XbeGpWNroo++zuX05Pw45NaD+K90GYuMvuWY6TzWH00ks6SKjw/EUVl71eds7g4FifQbqafBZbx4v4PXie9IRUGPm4vNKWOIvQmvz/HFWqHHO39G8VtoOjP97Fgw3IG43HLh451wUGTBZF4W5zJCRPp6GxkXae5LmKb6lKn3v8Obc5vG3CH2JkRnl4nPKbGpqn3esW+4T2c/+qs9oDyPqsGzOZx2mGW7l/H6ydcprL78G+wxXViMFLaug6K12tDSGUF2QZzJFp70JzNPEl0YzcP+DwMwzGoYFwsuUqfqPKDpeObxVtHO3WX7vO1sum0TTw5/kpqGGirrelZsNr4oHi8zr171RYuWG5344vguiaE3IpYGlhjpGDUWNA3NCSXQJnBA+cGPsh3FrsRdRORHsNx/eX93R8tl1kxZw4E7DmBpYMmOhB2U1Q7sTE2NCc+LFy9mxowZvPfeeyxatIj333+fm266iX/84x+aOkS/8PLxl1nwxwIe2f8IKnUz8W7wrcI7T8t1TZ2qjoa+iuqsqxL+tI4jYcGXQgCw9umxhUFWcTVWCj1emDWYr48lUV3XxX5XFcOawSJVPz9eFBgCYVEw7E4h6FQV96hPfUVaYRVO5l2zJ1gU6MivIeLHuqKmnr9irwPrjUu7hdfmuZ/Ea7Uadj4jRDSLbtx4yeRCLO6t53x5ruiTy+UHNYkEblsLR//TlNkRt0f8nf+547aSjsDJT4Xf8ZVJjUu7xPK4PZB1Hn5cICxeqq8qFJRzsWXEc+N5duAhNmSeKDS36W5hdwGQHnrZPuP31scI2SDes6uLWMjkQjyWtxE5LJPD1Jcxyg2Fe35v6o+eQhQ6vP1LUSBQT9F+P9sgsyKTjTEbefXEq6jUKmoaajictp/4snkYzP6GctSUNlQJ2w6PacKyB8R399KfUJ5HQXkt5ka6SKVt3KDWlInznfKSSK3f/6YQhIbfLVLih8xrarOnZIVD5DZ44qyIrj3T+yJMjYRvElGXpz4TfW3L83zk/RD+C/y8SBRzTDgoJhx2vwA/zm+6Jq5QXQK5MV06/B0jHdkff534PHeErR8YWrQW7ruJvakBERkluF8VIf7egqGMce+4WBdDF4OucVPUuvvUxvFhzmAlW8My2HUhi5t8rNkSmt5kswGQHweW3tiYKriYW8vDk9zZGpbBvWNcGOVqzheHr4p4N3dv+p3rIiHJhW3bdnQHlUr4xddVCCuKkQ+ISPNdz4msgx4Ww4zOKmOwrYKZfrY8Od2LACdT1u6PZf5weyyMdCmsqBGRzR7TxPvaKDyfExN3bfDdyWQWj3RimJNpC3/uwbYmxGSViqKUMbvgt39CdSnmOSdJmvwxvJQGD+zh35HrWRe+jof8H2Ky42ROZJwQDdj6waTnYNsjrY5pb6o/cIXn2koxzlzrLB8tLRhsNpj8qnyyK7JZHbKalYEr0ZeLcV+hq8BR4UhMQcfjd0FtAbmVuQy1HNqrvij1lDgpnJBIJNgYtl34sCvEFsfiaerZ+YZatPyNSShOwNPs7/s98bP0IyI/ggZVA1tit/R64kzTOCuc+XDyh/x060+dFmfVcu2QSCRIJBKeHvE0b59+mwmbJpBQ3LMs0GuBRg2YZ8+ezQ8//MBff/3FN998w6233qrJ5vuFU5mn2DJ3C7UNtbx6/FWW7Vomop+dRkNhshBstFy3rA5ZrZFq1W0SuVWIzuOfBqPLg7RDYI+LYaUVCd/jwbYm+Dko+T0svWs7XtolxLRN98D6KU0F0RKPCK9gpyAR2Zp2pkf96gvSiypbPAx3xILhDuyLyqG0uo6w1CIOJw1woUjVIArizf1E+DPH7hWic348TH2l++25TxER1NAz24TMc/DZaCHGujUrZGHpKSw3Ni4R4t2lXRD4T1GAsKN0nojfIOghYWVxJQI1fCOMfwYW/wAL1glhYsNMWDMEDr4j3pOGeiEwddeDdtJzsCJSRD5/NQ1Ks+CPx+HWj8BlnBBcr1BbCSc/hskvdO8YAEMXEX/bVjBz6XzbLhJTEMNYu7GU1pTy9um3eePkG4yxH4OnpRMJeRW4K91JLL4soHnNaDqXjDCQ6ULCQXT2v8okvXZsn/a9ISZJRz4gRL7CRLh9vfhcQQhViYd6J7akngavm0T0+cgHRAS1JooWqtXi2vj1PhFB314mgJmLuG5dJworlp8Xi/NbflSIbl/f1NIW69Rn8MO8LmWezBhiw2S3GyDtc9CtYmKkl9EzDmYGqNU0WWBcZoKXJUZ68o531tGHhw6I4ocgrr2Eg6BWY2UkZ6KXJbE55TwxzYuCilo8rYyaMnUK4sDCC0czA1wsDJnlZ4dcKuH2EY68PmcIv4akcfhSs3sxhb2YYKjtWrR6cWUti9adIr2ol6LoqU/g4LtNBfeMLERmRcIhuH+PEJ67MOlx9aRyTHYpg21NGl8vn+yBg6kB4zwssTDWo6C8Vvjz2w4V1/wV4TkzDBxGtGpfrVbzx/kM7h7duoL8IFthX6VSOsNT56C+mpqDH2Bel4PPyCkiywCIKoji5dEvM9djLpMcJ3E0vZnX/sj7xX3OVeOKg4asNsLTivnkQFyv2wHEfU/Er/C/J8Vn10+FdbUIZFIZczzmMG/7PAzlhkx3nt5i/XCrzu02zhWfY5zdOI0WdLMxsiG7smd2G/FF8VrhWYuWTvi7f0+GWg4lMj+Sby9+i45Mhzkec/q7Sy2QSCTMdJ2JnqyDejJa+o0A6wCC7w7mdq/bmwIBBiAaE56XLFnC0qVL2/y7XsmvyqdOVYe9kT1vjXuLBnUDeVV5JJQkiIg390miGFV3/UW1DBgu5l/kj4Q/Wkaza4qQDeIBrDntCM8fh31MWmlaq+UgHkLTCitbiLGPTfHg80MJXYt6vrgdpr0mBLplW4XNQfRO0Q/X8cJaIG4vbBsYqTNqtZq0wnasNoqSIT2kRTq/hbEeo90sOBidS1xOOc7KgVdMogVpwUIYGTxbRJyf/gwMzOG+/7Ud0dkZfouEkJ0VISLbI7d2vL1aLewWzm8UAuaW+2H2RzDr3yC/6r0LvA+8Z8LmZZB4FKa9CkonFBlHoKGutcVHXZWIxPVbKFLNU46L4oBpweDTrJp70EPCPuOJELj0F8TsFKKowgb0jOk2BmZw8zviuF9OFNkFvgvEMaOb+Y+GbxTfQdseRkJJOxHXukl0YTR+ln6sm7GOrIosCqoKeGf8OwyyMeZSThnupu5NM9ce0yHpmPi9yQiF4cvg5CdYRH3Pg7U/tm486ZjwEb/5XSGA2fpD0IMto8eVDsJ/N+wHOPpRz04iLRicxoj/HUaI6yI7omdtNSczTEyYzfsMJj7XOkK9OYu+hdvWiImb0cvFmCaVwc1vw4Rn4JsZ4vwa6kVEqrG1KFjZCXpyGbd4mXS63YAn6CEYtqTXzVgY6aIrl7bpid0lzFzF5wLCAkIqg0PvglrNY1M8eWCCG/4OSkz05dxesB4+cIKf7xBR9ZZeOJgaMNRBibmRLqdfno6rpRE2Jvp8cucInvstgvzyyzZAUqk4VidRz6XVdRRX1hKaIqxuLmSUdLh9p1z4TVyH3rc0Lbv5HXj4MDgGiu9wyvEOm1Cr1Uz49yFe2XaBqlrx+34pu4zBdk3ZFKNczdm/cjIyqQQLY13yy2vFd87O/yrhue2I57zyGupV6hYFIa+gNNDB1FCXtKJKMW5MfRndM58TZzAMfT3xwKlSq0guTcZdKQokTXCYwP+zd97RUVXrG35m0suk994bSQgl9BZ67yJeUewdCyr2a/npVbFcFUTFThdFeu81JEBIIL33Oum9TOb3xw4JQxJSiOLVedZiLXLmnD17MpM553z7/d73XO65Nn9BTR2hsK/MUxnbxliXwor6W85k2B+dz6bwzFaFen2TgubejnnsXRHeWiMXgbbh30BRx+GIav4cXh7yMjvn7uSzkM/atZoHWQV1WXi+UHqBEKeQm+7TU2z0bSio7nnAYEVDBRUNFdgZ2vXpfNSo+buRXP7PtdoAoXjenbKbLfFbeHfku0glfaoNVfMPQEuqxXDb4YTmhd7uqXRKn91Ff/rp308lkFiaiJepFxKJBA9TDz4c8yGvnH6FxJJE+pn3E0E9R98WCtInzon2TjX/MyiVSpLLkjHSNuJy4eW+TY/NOAfVRUJRfD0Og8WNznVEFkby7dVvaVY28+ygZ1u3bwrLxNZEl5/PpZNRXMPMQNtW+4nBLmb42srYcD6Dh0bf5HNXWybmsuA70G0poAy6D365WxR09ExFaJvfXHjfQbTm99AuoK8pq2lEKpVgrH+DvUJdBXw9WlhRFKfCiKeEzQEQ7GJKZFYZ9U0KnExuYstwu1Eq4eSHwlIDYMIbtz6mgbnwnP9pprCK2L9CdGQY26vul3tZfB4aa8VihLm7KAz4LxD/OmPSO8LCwNpPhJcNfgDTM1+BMk+E1M3+ou346N/BbiDIbMRnS54MB1+DwDtblXIqGNlC8IMQsx2c8kVR+FYIeQ2a6oQnskQCXtOEr2xFnphT2Dcw45Nbe44+JL4knjkeczDQMuCrCV8BQlXgbWNEQn4lnu6eJJW1KPsMLcHcDWJ3CK/nEU/BFwO47P86Tok/Qvxe0d4fuVFYa5z/Csa9BHom4vi7Nov35EY8JsCBV8Rj1v7gffOk63ZkhYsFCTF58VnY8QT4zhLK8p6qbEvTYc9yMW+/OaoLFp1xrZhpaAlT3lN9bOC9YhFk+2PCG7uuAh46DN9PFr/HWV/0bsHnH4hEIsHeRK+d4rlXSDXg3p3w/WQMsMPJ7wH87MQ56pHhNnhe3iFyEQpiQdcYTJyYFlBGk0IUGK8P0xziasbCQQ48vzWKqf42TA+wxfia3UYni0yFlXXctfY8nlYyXC0NMNTR5GpOOdMDehmYU5IqFtmchqtu19Rps+9xGSnECoMfhE4Wu4urG2hoUlBW28j9P4Xzw33BxOdXqiieAeExD1gYalNVVQ5lmcKzXSIV2QHyJOHHb6LaoVHbVMv3UVvwtvbp1D/Sx0ZGXF4lzuYGVJv6Eq8zlFqXNuVpXnUeMm0ZhtpikdBK3wp/C38+ufgJK4JXiHFNHEX+gLFD63E6mhoY62tRWNm7QNPHN1ziuUlehKUVk1deR05ZLQ6m+ryxIxpTfW1eme7bswFrS8Xi6wuJoN1ShO83XyyEWi7v1RzV9A02BjYdbh9uN5xPL33KiawTjHMc1+7xqoYq4ivjGW0/uv3Bt4C1gTUFNT0vPKeUpeBh4qEuIqlRcxNqGmvIrMhsXcz8J9LPvB9DbYfywuAX1AtVanrNUNuhvHH2DRoUDWhr/PWEeH12JrS3t+/03/8qiSWJeJt5q2zzNL2uEOA3B4Y/CUH/gvDvbsMM+w6lUsmulF2qIYp/ZfqglTuvOg99TX0WeS/i3fPv8t9L/+2DibVw/D8w5sW2gsg1zNygvkLFomVN5BoWey9mX9q+VuX1zsgc1pxI5sP98RjqaFLT0MSpJLmK/cRLU31YfTyZjw7Gtw9XukbCfpFor3vdTeuwx2HB923FTxAetlY+nYe+/YnE51fiZtmB6jXpEDgNE+qxf/0iVL4tBDqYcCW7TCieTf5iX7T1VcJvFiDmd6HWDn6ob59j+FNC7bbgW/GdtOVfqm3mZz+HTYtFO/GOx4S6+e5f4bEzbUXDzpC2BNzdKfyov28uprwiVfjvLvoZjv6fWHyrlsOJ99ssETR1hAI24+zNLUR8Z4tizJnPxOu4FaRSUXi09BI/G1rC0MfF6768Qah9XW7NN01e23fheXElcfiYCWuRaz5dAN7Wot3d28yb+JLrWvNDXhevxTZQfJfM/45w05mccHkWDr4qiqlJh4U3d/oZsaB0DQOL9t9HACOfg8fPwpzVcOCl9p7ILejJrwobFRCq4RMfiGJ3U53qouvYl8T7HbsTrv7W/V9GVZGwQjn9CSgaRFfG9fO/Fczdhc1L8hFx3jZ2gCfDQZ4o/LLVdJtl4z0Y5mbWN4MZWEDwgxhlHRPvf0vQ5VPmEWi4jBQBhEF3iYVTqQbD3MwZ5WnR4VDPTvTE0UyPH8+msSsyRyzSFHfsc6dUKnl8QwQTfa05myLnaFwBdwx2ILqniuemetGBAxC7Syy23Ky933mk+D488LLwfe6AdHk1bpaGfLF4AE5m+gx//xj62hqYGXR8XjM30MGkQnhgo6ktzuU2/uKcYz+w3cLP+dzzbEr5FAerzq1mfG2NiMktJ7u0hgVfnWOr+38IXvBc6+MpZSm4G6sq01aOWUlYfhjrYteJDcaObWGo1+FgqkdOLyxNGhXNHI0r5LvTqSTkVxLibcmF9BKaFM0cji3gl4tZVNV3HSaaUpbCB+EfUFNbKmyuXMe0FZ1BiAWut2dS85fCQs+CVeNX8cbZNyiqKSI0N5TdKW1dTWdyzuAj82ldFOkrrPWtya/umdVGbVMt3175loFW7e1u1KhRI9gUt4nhm4fjZuyGnmb3sn3+juhr6fPpuE/VRWc1t4SxjjFuxm5EFkbe7ql0iHoJ9iYklCbgZeqlss3L1IvE0pY2PD1TGLFMtPJGbeqWZ+RfEaVSyUcXP+Ltc2/zecTnt3s6XZN4EDbfJYrP51ar2C50C6USUo6RWJKAp7YxS5Iv8phJINuStvWqla4dCfuhPEt43N6IRCIUUaknAMivzieuJI4VwSsw0DIgsjCSosp63toVwzf3DOLAs2NY/a+BhHhbEZVVhqNp20nZ01rGL48M51JGKVvCO7bpIHZH++KNvplQOd+ITQAUXO3li+47jsUXMM7Lsv0DcbtEkRJE4a0wrtXmJsDBmLi8ShIKKv96Vhta+tTF7xOK4+P/ganvi+JAX2LmCvftAW0DcvsvotrCA468LR5raoCzXwj14MPHhe2Kx8QeDa/U0KZB14iTWSdZfeVr3nL0Qjn9IzHOoyeFt/R/+4lCkdOwtgNHPgMLf2hT3XaEgTk4BItiSYsfaX51/q0HfV1jzAtCXXjpJ5j0DlWN1dy5504aFD23SAovDWf81vF8d/U7ooqiel2EPpd7jg/DP6S2sRYHQ4d2j3vZGJJYUImPqQ8JJQltVkCeE2HE022dFIF3UFjTTLnjRHgmCl5Kh/nfCp9SrymqC06dYWAuCrMeE4Ta79sQoZ68HkUjtuHvigWGfS+Kz1ZDNWx/XHjEX1/c0tIVvtJzVsPBV8TvvbGl0KRogt8fhepi1fErC4T//DejhTXKonWwIlX8nfcVRrawdDeMblEy6hgKRXy6uvDcE+YPdMBEvw+/Y31mYph7Go69A9segpMfiUWNYY/3aBhdLQ3enRvAsvGenEgoEoshOZfg/NftsjiOJxRSVdfES1N9mORrTXJhFfcOd+FqTnnPvneubBVWIM3NYiHUZ8bN9zd1AamWWJCJ29Oh6jlVXo2bhQEaUgkfLgjk4LNj2PTwsPZjtWBuqI1TbZz47r3GyGeFxcaEf7fbPyw/DC0MqdQ6x9GMox1afI33tWJjWCZTPzvNwkEOfHDHQHS02s5ZaeVp7VqijXWM+XL8l/wY/SPheeFicac888ahcTLTJ6O4ptPX0xkZxdXo62jw26Vs/GyNGO1pyYX0Ui5mlGJnosdIdwt+u9jJdVBdW+Dwscxj7E/dy70bhlN4+LX275nLSOGXXVva4zkCXC26qrpYqKbP8bfwJ8QxhN2pu1kduZpVl1e1niP3pu5liOmQPn9OGwObHiueV15YiaGWIc8MeqbP56NGzd8BRbOCdbHr2Dh9I1tnbb3d01Gj5m/BGIcxHM86frun0SHqwvNNSCxNxNv0BsWziSdJpTeEmpg4idb2hP1/4uz6jqiiKI5lHmPHnB0cSj/UfpUk+YhoR/yrkHwEEveL1unD/xYekfLkrpVrORGiHbYgGtbPIyn0v3jmxqBjZMuU+OMMsOo6tOSm7H4G9r4AO5+CeWtVvVSvx2+OsDoALhVcYpD1ILQ0tJjhNoOdKTv5/kwaMwPt6Gdn3HpIiI8VAA43eDJ628hYNt6T3y61VxZJG6qEzYb3tO7N3yZQ3HDdRpRKJYdjC5jkZ636QH2VCGjybgks1TYAU1fxXgKGOprYm+qhpSHFRK/vAmX6gmpFLVMtdKnb/igKHUOUzn2TVCyvlXdYJHnu5HK22LqJ4C4QSnELL1FcNLQUfq89tD9YdXkV434Zx5vn3uSTsZ+QqSHhzrQt3LnnTj66uhbmfQWPnha2BdfjOUkE/HVC6/xnfNx6bEVDBfN3zedI5hGVfXOqcnpXjNbQEt7mDx8Fjwmcyj5FbHEsV4p65kOcW5XLN2nf8N+Q/3Is8xjPHX+ONZFrej4f4Nsr35JXnccCrwUdtrtbttgINDbpYahtSHbldX/f419rK54C8qoGLGUtrfwSiSjgj3wGhj7W84lNfFN4el/8Ufzc3CwKd7ueplHfShSTY3fBkm3CP/mJUJjyn47Hsh8klKqxu+DHacLuJHIjXNkivr+voVTCr0th4D0w9mXhiatv1juv766w8Rf+ztdwHS28sNXcPkydadKzEufEJb9D+FqYvrLXnQmjPS0ISyuh3sRDXJOlHIM1w0QIJuI757+Hk3hukidSqYS7hzkzztsKVwsDdDSlPQsYjNoCdWWQd1l47N9os3EjEgm7DedzdcTnoGeKbgcFyjR5dauViUQiwcZYF/eOOoBaMNTRZAyXqHdtW0y8bGbLae9xNFn3Q14r58mjT3I08ygAYXlhGNUu4FLpbp4/+Tyb4je1G3Ogkylhr07g0HNjeGi0W7vvqJSyFFyNXdsdZ2toy0tDXmLt1bXiurisfSHYyUyfzJKeF54T8qsY4mLO+Ru7AAAgAElEQVTGCHcLhriaEexixumkIraEZzLJz5oHRrnw47n09v7RVUXwiXfrXK4UXeFV4yCmyNy5196WEvexqvtr6Ylz1rXzZw/Ir87nqWNP8eLJF2lsbuzx8Wq6z1yPufwc8zP51fnItGVcyL/A5cLLxJfGM9qib202QCiec6tyu72/UqnkVPYpnhzwJFrSv7D9mxo1t5EzOWcw1THF38L/dk9FjZq/DZNdJnMo49Afk192i6gLz51Q0VBBdmV2O1WHlb4VCqWivdLNbRyk/2/ewB7JOMIs91k4GjnyYMCDbI7frLrDyZXw82xRxPwrkH0BRj8P0b/BPdtFYeOHyaIF/frCVLMCaf11rbOHXhft/BnnwGsqSRXpePZbLNRB8iQGWQ7gUkH74L/ukJ8TzvHU/RRr64pEe6ehrY/tTd3LL/G/tO3sPU18VuoqWgvPIC6kD6UfZvPFRB4dq+pzNcrDgn52RtgYtfciHe5mTnltIzG5qm3Chrmnxc17d1SPIPxdb3PhOaWoivqmZvrZXTfn8hxRuApYKNSZ17AfKALIWgh0MMbD6g8oVt0iBloG+FkGsLcuh/vNDNiZugsQnmZPHHmCFadW9OiG5hp37r6TgxkHVbZFy6OJLY4lobFM+I3WlIhi34C7ez3/0NxQdibvZO3ktbwY/CLjncbzpu+bvDL0FV4b+hq/J/1OSV2JsLe4/v3pgqyKLKb9Po2imhZ1opHwVt0QuwFtqTYH08Vra1Y2807oO0zdNpXTOb3/js2oyCCrIovDGYexNbAlPD+8R8efzT3LAOMBTHCawKYZm/hi/BdEFvW8lSm/Op+ksiRWjlnJ84Of73AfiUSCr60RF9NL8TXzJa4krtPxkgurcDa/wXN30tsizKw3BN4pOiWUSvGdeXUrGFiQP/gl6DcXlscKhT0IH3Gz9gWoVtzGiSK1z0z4eqTIRBjyCMTva9snbrdQT49ZAYF3qFoA/dHYD4LiZOF9rua2UeK5SKhz3UOE367fnF6PZaKvjbeNjDCFDyyPg7u3wpw18NuDUFNCfH4lJdUNTPYTHrKDnE35fulgAKb0s2HBV+c4FNONlvrSdCiKE10CZz4Tixod+dhfh1Kp5O3CsURI/cFrMoZ5Z9uFU6fLq3G17L6HtqS+ggHSJOTWI1u3fRHxBR+Ef8CEXydw9967sdG34eMLH/PyyTfJrsgnP8eHJ/o/zeoJqzmedbzDBT0tDSl2Ju3bnusV9cQWx3YaAjXcdjjR8mgURnbkl6W1G9vRTJ+s3hSeCyrxtpHx2eIgngzxwM/OiJmBdlzOKmN6gC0DnUwx0dPiWLyqup3iJGisgdDVKLMvcaUwgv5pYTw8/DUGO45lR+ah9k/mN0eo2XvIe2Hvcbfv3dgb2rM5bnPXB6jpNQOsBiDTlrHAcwHzPObxZeSXvBP6DssGLENb2vddb+4m7hTVFpFXldf1zkBmpVD7O8luEoqrRs0/jMKaQhXx3uaEzSz26aA7WI0aNb3G3cQdI20jooqibvdU2qEuPHfC8czjDLUdiq6maqFPIpHgZepFXPENhQCXUcK7r5vUNtVyOvv2F6qVSiVHMo8w0UmoZaa7Tud09mkqGyrFDopGUYyc9ZlQGDf1vD29T2msFQFRY14UN5VuY0VI3sIWhV7edX9kR9/B6WRLi1tNiWi7TTqMMu00W62dOWsoIyDoPlGY1bdgoJ4tEYU9V3bnVOUw/+hjbLS0Y5b8GPu1FK2P1TXV8fHFj/k59mcePPggz594HoWOTChq4ve2FZ4zzmGhoY+jXiAuzgkqXs4ABjqa7H16NBrS9upIqVTC4mBHPjmUqJLsrlccLfwLu4t1PyiMv63v8ZkkOeO8LYXCKvtiS2v+I0KFOeOGAFP7gW1K/LwoZptlMcT59gYjdsZi//v4wMqa9KZKDqWLG933w99Hpi1D0axQ8SjsDrVNtRTVFvHl5S9pam5CqVSSWp7KD9E/MM1lGomlSWAXJBR/GWdvySv318RfWTZwGf4W/sxwE23JJlomDLAaQKBlIH7mfsTIY1r3767S68eYH2lUNPJZxGet23Krctkcv5lV41dxNucsNY01xJfEE5obyv397ud83vkezb1R0ciH4R+SVJrEo4cfZemBpYTmhfLswGcJywvr0Vgx8hg8DD1af/Y28ya7MpuKhoqbHNWefWn7mOg0scvQh4fHuLHyQDyeJsLnOT6/giaF6up1VX0TGcXV+Nl2c3GpO9gNgOYm2P00pBxtVTc3XrMEuZmHbUdIJMLu5P4DMOFNEXyYflp8lzc3C1/wkNeEP/efjaaOCHw990XPLZvU9BnlbjNFJwb0PIyyAyb4WrE/Jp8KLTNmrz6D3D4EvCbD2c/ZeyWPGYG2SK87l15T9L4zx59nJ3qxIzKn88ELYsWizJWtoujsHiJsoFy6Vllml9Yir6qnqLIevKZhEfczvGcD+1YIuyKE4tnlxoWkjmhuhh+nw/6XidPqh7xBqCqblc3El8SzcfpG1k9bzxvD3+CN4W+wecZmQrOiaK51JcjRnAcD72WknShWt+aWdEFeVR7Tf5+OrYEtfuZ+He5jomuClb4V0TQwX5FKTHGMyuPOvVY8V+BlLcPCUAcDHU00pBJemurDyRdD8LKWIZFIeGCUKxsPnWVLeCZ1jS3XYfIkcB8PlzeSvfUuNGvLsKmvAcdhLPBawI7kHe0L7/3mCoV8ecvnIOtC6/vTGc3KZi7mX2SR1yKWD17OTzE/tTsXNjU38X369z1+7WraI5FI+HLCl9zvfz+z3GcRaBHIEt8lzHTrRhhtL9DW0Gai00T2pe1T2V7dWN3h/uH54QTbBHca4KlGzT+RDbEb+OjCR4AQn8TKY5niMuU2z0qNmr8fU1ymsDlus8r1TWRhJKsur+rxvXRfoi48d8KB9ANMdZna4WMj7UZyIuuE6karfuLGtbJ74ROnsk/x7vl3b3GWt05CaQJAq5e1ia4JQ22HthbHKIgRqegBC0W7/qWfbtNMW8iLAktv0Q55rWXae6ooQPvNFUq9ZoWYd8Q6NGvlogU26ZDwo9XSJS79KN/Iw/lx6o9t7aJWPvg2NPa8kFRXzvaT/2ZWTT3fhXzB5yGfsyZyTWt7w47kHfib+7Nl5hYW+ywmtTxVqKqDH6b4zEcU1RTibeQmwuBSjmHUMIZSzSPUK+p79Gt5dKw7lXWNrDqW3LpNpzIDLDy7P4iukSg6XWuDL4iFY+9BRc/VuL0ls7iS/oYVwufzx+mwejA01YrAuhsv4B2GCHuVugrYfBfj4t/hBfmbfRI82deMsh9FiNNEvpm0lojCCA6mH+RSwSXeHP4mk5wnEVvcs1DH3KpcnI2csda35oGDD7Bk3xIePfwojc2NvBD8AtlV2RTbBrA69F2avWfckm1BbHEs/S37d/q4v4U/0XJhebIlfguPH+7am1VeK+dg+kF+mvoT53LPkVqWSlVDFU8efZJHAh8hwDKAAIsATmWfIiwvjFH2oxjvNL7HxeLjWcc5lH6IRbsXMdZhLM8OepapLlMZ5ziOuJI4apu631YfLY/Gw6Ct8Kwl1aKfeT+uFvWsS+B09mkmOnftsR3ibYW3jYy8QjMu5F9k5hcnmfHFGfLK2+Z8JasMP1sjtDX78FQukQjFX9IRUXTWM+2bcS29YNBSYaNh21/48x94GQwshR/17WLCm5AfDTufvH1zUNOn3DHIkb1X8lhzPIXY3Ao2hWWC5xSU+VfZdzWP6QG2nR4b5GhCUkEneR0px+Gr4eK8E7UZ+t/VZq/RDWuQiMxSpBIorKwD5xGkTNsCLyQJ//+Y7TQ3K0kvbrPauClZ50V4X8YZImXjKKkWC8YZFRkY6xhjomuCk5ETo+xHcTi2AF0NGcq8x/ly8odseUTMWSKREOIYwvHMm3sBJpQkkFeVx5qoNcx2n82qCatuGgIVZBnEZ6m/UymBWLlq4dnJvHeF58SCKnxsbr6wPN0Ffih7iMNnz7PhfIbYWJws3ptFPxM19W36O44RYblSKUGWQSiVyvaqIG0D8J8vAmnrK2H9XLG4cBOyKrMw0jYiLKWOnEITHGWOnMw6qfoaShOJqYzpZAQ1PcXZyBk9TT2MdYx5IfgFFngtQCr5425rZ7jNYG/a3tafk0uTWbR7UYf7Xsi/wBCbvveaVqPmf5lzueeIKIygrqmOXxJ+Ya7H3HYCPzVq1Nw6d/veTU5VDu+FvUezspkdyTt45vgzNDY38tKpl4iRx9DY3Pin23GoC88dUF5fTmRhJOMcx3X4+CTnSRzJPIKiuU3ZilQqVKzdVD1fyL9Afk0+jYrb5wOnVCpZdXkVcz3mqqzKz/GYw86UneKHnIuiHRmE/+eplVCSdtNxm5XNHM883t4L+xbnCkBWmAgi64h+84Qf6X/s4fspEPIqZe5zIOxrcZPoPR08p3DAxJzZnvNUgyOtfNGSJ+Bn7kd0UXS356XY/zI75ZeY7/svsA1ksPVg9DT1OJNzhsbmRn6M/pGHAh/CSNuISc6TmOk2U1gIeE7khJEpg7Ut0Eg/I4JsiuJIzbbDw9SD768KVUxFQwVfR33N11Ff33Qe2ppSVv9rID+cTRM3tYB2RYZYLOgJA++FiHWQGQbr5oiQsa9GQF1518feKkol0xNeZ/7FJSKo6enLMOwJmPt1xypLG39hD/LzLLDyg8dDobYU0+Rtf/xce4hUImXl2JX4mvsSaBHIa2de4+UhL6OvpY+fud9NrRQ6IqcqB3tDe74Y/wUP+j/IgwEPcmD+AVaNX4WVvhVORk58psjnGwNN9tp5dD1gJ5TXl1NWX4azkXOn+/hb+BNdHE1dUx1rr6wltiSW5NLkTvcHOJh+kBDHEByNHAlxDOFMzhn2pO7B2ciZJb7CamGe5zy2JGzhfN55htkNw9/Cn9yqXIpri6lrqhPhfF0Ujn9P+p3nBj/HttnbeGHwC8x2n81bI95CX0ufQdaDeD/sfTbGbeTLyC87HeNwxmEyKzLJqMjAWV/19xBkFdRju430inQ8TLr3nkwPsKWk2AUpushcf8TZXJfQlLZgvojMUgY43SS4sbeMfQkeOS4Cwv4IZn4GSQch8xzcub5PVK69xn4gzP5CnFuuX7Qqy4KEA7dvXmp6jaVMh8n9bPj6ZAof3RHI+vMZpEkdqcq6SoOimf4Oxp0e62ZpQGZJDQ1NN1yM11eKLoCARcLWS6IhPjtmbjBwqcj56AClUtnaiRSRUUqwixmFlfUgkYguAgNzsShenER2aS1GuloY6HQjfDZmh7ClWXaZWOtZyKvqUSqVLN+xB08Tn9bd6hoVPL7hEq/8fpXGJk2GOqv+TQfbBHO5qPNsi4SSBB4+9DCL9y7mVPYp7ve/v8upDbAawMWiSAIbmoi9ITfDWqZLWW0jdTf+fm9CbYOC3LJaXLooyGuVpSFFwUf2p1l7KlWonouTwdwDpft49uSeJNhxjOhyQBTeHwx4kBWnVrS/Xh3yiPAbP7eqJZC64+J8WV0ZV4quEFsci6+5L7siczkcW8BCr4X8mviryr5RRVF4GfbwmkzNX4ZB1oMorysnvTwdEO9ndlV2O2W7ollBWF4YwTad3KuoUfMPRF4rJ7c6F29Tb45nHWdXyi7u8L7jdk9LjZq/JTJtGV9P+pr4kngeOfQIqy6v4uepP7N80HKWD1rOK2deYdq2aXxy8ZM/dV7qwnMnXCsKdYSTkROWepbtbRncx0P07+0PyL8q0tpBtP011hKeH44UKXnV3fML60saFY0s3b+U+w7cR2ldKQ8GPKjy+Ej7kWRUZJBRkSGsDK55hdoEiILE5sU3bTuMK47j9bOv8/Chh/k18VeUSiVNzU0d71xfedO5KpoVrL2ylqnbplIdvQ3Ofi7aWzvCLgge2A8vJsGr2TDkYcpcZ4oipr45+M5CGXgnh2Sy9mp2Kz8ojMfP3I/Ykm6qT9PPEpF1ElNTD7zHvgaIG5l7/O7hy8gv2Z60HQeZg4padIrLFI5kHqFR0cjP+lrcnRUHZz8Dm0DqcmIoqqzn3VGvsz52PVUNVXx+6XMuFVxiU9ymLoPVrI10mTfAnu9Pp0F9JZL6Ci6VdfwZ7hS/2cKSZMu/hFf1/G/E+54R2rNxWjiVWNT9QLgL32Fcl03Cv86LIDhjexj6iFBKdsbU90GeCGNXgIYmzFuLRn3v0uj/LGa6z2Sc4zjGOAgbFEeZI1UNVZTWdT7vLfFbVIqs2ZXZ2Bvao6+lz1jHsYx3Go/GdcV5b1NvdhRdZJmOM59l7KOmsecKM4C4kji8Tb1vqiIKsAggWh7N2itrCbAI4C6fu9rdcN/I2Zyzra9/iO0QwvPDOZV9iqmuU1sXwSY5TyKvKo+L+RcJtglGU6rJYOvBhOWFsT9tP1vit7S27HVEXlUeMcUxTHSaiJuJG1o3hH1+PPZj6hR1nMo+xcbYjaoLiS0olUrePf8ujx5+VIxxQ0jQQKuBnMg60W17kerGaqoaqrDSt+p6Z8DNwpBMeRNL3d5BqlWGmWk5eeVt370RmWUMdOojRfL16MhAZtP3417DwgMePAwPHwfdzouAfxoyG9CWQXFK27bL62HvcvgLhnOo6ZrHxrrz4ChX5g1wYLSHBQ9sz0e7sZKNS3xu2v6uq6WBnYke6cU3tNAfeQtcxoiATUUDBN0lFkwkErFwod3xufbTw4nM/+ocxVX1hKWVMNXfRlhtXMe+PANyU6L56Vz6TdXYrTQ3CwWu31zQ0MRCpktxdQMpRVXEFsdiIm3LiLiSXY6tiS57r+Yxwdeq3Wv3NPXsdKEwtTyVJ44+wavDXmX1+NW8N+o9jLS7tvUZYDUAmZaMJzWsiC1UVRNLpRIcTPUoqOzkmrADLmeV4mdnhJZGF7csJangPgGz1J2MsIWfzqW3Fp63JmyltK6UhZ4LVQ6Z6zGXRwMf5dUzr6qOZeUrFuJPfghT3oWUY2RVZPF26Ntsid9Cs7IZea2c+w/ez1NHn+Jy4WVx/ZhXQUZxNZNdJnOl6IrKeT2yMFJdeP4fRiqRMsxuWGubcnRxNM3KZgqqC1T2iyyKxFzPHEeZ4+2Ypho1f0lCc0MZYjOEUfajeDv0bSa7TFb/jahR8wci05bxzaRvcJA5sHbSWlyMXQCY7T6b8Y7jeXXoqxxIO8CF/At/2pzUhecOMNYxZo7HzcNtprpOZVvSDcrKAUtEy2TWDaFVpz+Fq7+Km9oNCyg6v4ri2mIGWA8guzK7j2ffNXvT9iKVSFnotZDPQj5rV0zRkmoxw20GO+N/gbTTbYpngOCHRBtiVuct79HyaCY6T+SnqT+x+vJqZmyfwfITy9vvmHYKPu8vbqJu4Pek31l5YSWzd8wmLC8MV4k2v516A+7+DZxvkhxvN0AUTVpoMrCFFamw8AfQNSJGRwstfQtVtTOApQ8Uxokbh6wzwmaiK6I2k+w5Dn/LQJXNM91m4mLkwrvn3+WhgIdUHnOQOeBh4sHd++5GV8eIIWPfEn6n416hIS+Gwc6m2Mls6W/Zn9C8UE7lnOKVIa+graFNVmX7hPgbeWSMG79czGLd7iMkKWxYf77rY1TQ0hMF5wcOgGeLHYDLmF4FZ5ZWN3DvD+HE5HbDukSphNDV/FvxMPaWZt1/EjNX0ars2NLSaOGB3P/hHs/1z2S2+2w+GtNWMJVIJPiY+6jYbWyI3cDZHNE9UdNYwwfhH3A+t82TKacqB3uZfafP4W3qjZPMiYfu3IWfuV9rUF9PiSuO69TL8xrW+tZIJVJO55zm5SEvs8BzAXvT9na64FCvqCeiMIJhdsMACLYO5lLBJSIKIxhhN6J1P02pJkv8luBr7tta7JjvOZ9Vl1exPm49749+n/N55zv1x/4p5ifmuM/ptI3PQMuAlWNW8s2kb7DUt2y1HbqelLIUdDV0MdAywN+8fer2CLsRWOpZdnvFOKMiA0cjx263A7tY6JMmryZDXoeltjvNWlmtVhtKpZLLmaUMdP4DCs9/BhIJ3LAYcFtxGCyCa6+Rdgqqi4RXvpr/OTysDHljpvju+vTOII6vmICOnR/OisybHyhP5mPlJ5j9tkCcl+J2w97nhV/+lPeEL/i9O2Foe0uhzOIaonPauoPkVfWsC83AxVyfof85irWRLhN8rIXi+Tr25uhTmRPH75ezeTKkG90QBdHiOqxlUdbMQJviqnpOJBQh1c1B0timao7ILGWCjzUfLQzk3uEu7YayN7SnoqGincVYZGEkDx18iKcHPM1Ul6kEWAYwyr5rOxEAF2MXDt9xmAHe80mrzm3X2edkpk9eVfe7/c6nljDUtRvBtSVpwvrEcSiv+Zfxw8kkmkszSNWQsjpyNe+Pfr/dAiSI4nNxbTGp5amqD4xdARPfhkH3g6YO2yK/oaimiB3JO/gg/AMeOPgAk10m08+iH78l/oarzIuM4mrS5TXoaOgwyHoQYflt18pRRVF4G3p3+3Wr+esxzHZYq+VXjDwGAy2D1nDohJIEtidt50jGESY5Tbqd01Sj5i9HeH44w2yHMdphNBoSDZ4MUtubqVHzR2OgZcBbI95SCYSWSCQ8O+hZxjuN57Vhr/F/5/+vc4FoH6MuPPeSu3zu4kLeBa4UXWnbqKUH41+HzXfB+nnQUAOlGZB6HALvhD3PQXURF5L3Mch6EE4yp24VE/uSZmUzP0T/wGP9H2OA2QS0lB23ac9zmsym2PU8YGNOqcl1K5ISibAUuVnhuTiafub9cDF24bvJ3/HOiHeIKIxQTYNWKuH4f4TFhDxR+Ai32DkklCTwyYXPOZvQwGSrp/ku+HWeTY9lnZkl9Tb9bun1X/OLbad4svSBylz8tEyJLYiA3x8WXtE3Q55IlpZWuxVbiUTC/438P94Z+Q7DbIe1O+zriV+z0GshLw95GcnAJbAsAtxD0KtMZ4SrKLCNdhjNT9E/IUWKq7ErgZaB3UontTPR45dHhlNfkIDC2IWrOb2wyPCepuINfUUrAGXaqR4PczFDKH1OJRV1vXNhHM0KBZEKF0z1e1iMavEvViqVHapW/4rc+PnzM/NrLTzXNdXx9ZWvefPcm1Q1VBFTHINCqSA0r011fs1qozNmuM3gP6P/g1QiZYHXgvaLZN0krjgOX3PfLl/Lt5O+ZcP0Ddga2mJnaIeWVIv86vZ+99WN1YTlheFl6tVaTDbXM8fW0BYfM592arolvktYM3FN688hTiGMsBtBXVMdk10ms2r8Kj6++DGRhW12F/JaOanlqexJ3cN9/vd163UOth7c4YpveH44Q22HsmbiGh7r/1i7xzWkGnww5gMOpB1oX7TogMyKTJxlnduW3IhMVwtDXU3C00pwkXlTrcwgr6wthExfWxNrI7U/Xp/gECyspQAaqkU2wNDHkGUdu73zugGlUsn62PXd7yRR04aVLxR2sai851kaTdxQVpfApR+FrYaxA9y1BfRarpcsvTtUOP8Wkc1nRxJbf157KpW5QXb8984gzr0ynp8fGIKtiS6l1Q0oWuw3GhXNhJYZ465ZxAuTPLGU6XQ8r+vPbQUxYNO24B3oYMyBmHz2RaejrZ9PVXmbajoiQyxOzQmyx8u6vUeyVCLFw8RDRfV8JOMIzxx/hrdGvNWlCKMzDLQM0AtYhENjI4lFV1XOzS7mBuRUdL/wHJZazDC3bixIl6SKxWjbICwr41g5yYQchRHPnnqDZQOW0VBrQWZx++4fDakGU1ymcDDthgVaLT0Y9SxIJCjdxnEg6xhPBj3JlxO+JDQ3lNnus3m8/+Ms8V1CY3Mjmk1O+NgYUVRVT12jgmG2w1oXjItqiqhqrMJWtxuKdjV/WYbaDuVCwQVqm2pJK09jpN1IcqtF4Xl3ym7ePPcm25O3M8lZXXhWo+Z6Mioy8DDxwN/Cn8MLD2Oq+z8qmlCj5m9EiGMIFnoW7Eje8ac8n7rw3EsMtAx4dtCzPHjwQUZsHkFxbTEX8y+y01Af7tsrLCTSz4gwvqC7Rcte2kkY/zon6vIYZTkAB5kD2VW9UzwrlUqWb41EXtWzELofo3/ETNeMITZD+PfOGMZ/coLlv0Ty5KaI1nAaAM+aCg4rrLFyGMaWxF9UB3Ec1qXiuZ+FKBB7mnoy2GYw01ymsT15e9tO6Wegukj4MmdfgO2PwS9LoFnBmsivqMgfxd2WI9l1vJrEC4fwcRqDs6l7qwq0t0QVRdHfqoOgNC1dCLob5yPvUiZRUqZjAKc+hqpOiqZKJRQlkNVc12GrkLaGdjvv7Nan0tBikfciBloPFBvM3alDm1ylGXOdxPs5xmEMV+RXGGk/EolEQn/L/kQVRfFFxBdkVtxcseVtI+Nh3yYsHdzJLaujqr77q1jXboavUV7byIJd9SiLU6GmpNvjAFxIL2GAkwknE7pReE7YS5nTROxN9XudAv5l5JesvLCyV8febobZDWNv6l6amps4lnkMPzM/RtiN4Ouor4kqimKw9WCVFNrcqlwcDDv337XUt2y1eBllP4rcqtwufZdvRKlUElkU2aHS90Y8TD3Q0WgrmHiaeJJUpuqZeSH/AlO2TWHZsWWtNhvXmOg0kWku09qNK5FI2hWjXx76MuumrUMqkeJu4s5TA55iXew6FM0K3g59m9k7ZrN4z2IW+yzGQs+iW6812CaYiwUX222/kH+BYJtgrPStsNS37PBYI20jZrvPZkdS1yftjIoMnIycujWna7haGHAmWU6AhR+FDamtVhsRmWW35O+cVZlFg6Kh6x3/KTgEQ+wu+GEqnHgfbANhwD0YFF663TNrx8a4jd23hFLThpUfFMaJLqvIzULVfn0BP/0slGdROGg5u40WwZ7lMPgBGPWc+Dx0QXZJDeFpJTQ3K2lUNLPtUjYPjHJFIpFgJRMLRFoaUoz1tCiuFuf6dHk1RsZmaOrKWOKnDY21ELdHtdBcmg4fecCn/SDrAhTGgHVbJ8oIdwuGupoTU3YeH9MAUgpFF5lSqSQis5SBXSLEuxIAACAASURBVHxPeJp6klwmzg9p5Wm8E/oOX038qt33dI8xsiVQ05i7Di7l5ZPPt272tpGRUdq97566RgVXc8oZ7NKDwrNdEORFMs5ETpytM2nFlcxync9HBxJYezqlw0Onuk5lT+qeToOdIyw90Wiqw8fMB3M9c3bN3dXa0TbCbgSrx6+mKfEiQ2yl2JvokV1aw3C74YTmhqJUKtkUv4mRdiP/0PA7NX88VvpWmOma8VP0T7gYu+Bq7NqqeI4qimL5oOWMcRijoi5To0aNqmCmMytTNWrU/LlIJBKeH/Q8Ky+sZMLWCdQpOrfS7QvUV0C3wCz3WRxceJBR9qM4kH6A1ZGr+TB8Jd/mn0bpNQ2SD0PsDvBfINr/gu6mLuAOzujrMqG8FEdDxw6tNrrjx3o1p5zfI3JYdy6dY/EF7IrK7fKYi/kXWR+7ng9Gf4BEIiGpsJK35/gz2MUMGyNd7vj6XFuRsjAOmXUgjwQ8wi/xv6hejDsOETc/kZth/0utm7Mqs5DXysmpysHLRNXK4g7vO9iWuK3NpzZ6W0sgzzARMpV7GRSNJJx4h0sFl3HQCGFR2r/52f0YKRePoHQaJkIdM450+To7o7y2gXPZlwiNMex4h6GPIc08j4+eDbHDHoTMUPgyGOo6sIqolgOQXVvQJx5Vh2ILKNJzw/LEy/DrfTjKHPEw8Wi98etv2Z/tSdv5KeYn1sWu63rAogSajFzwspER2x2rC6ChqZnRHx4ju7Tt8xebW0EjmuTJ/CHz/E2OhqwbkurD0kp4erwn0TnlXRe/4/eRbjEOB1O9bs219TnywlgXs46EkgTWxa7jcmHnIUl/ZUbajcRC34I1kWvYGLeReZ7zeDLoSbYnb+d09mkWeS+irK6sVUWcXZV9U8Xz9WhKNZngNIGzuT1btIkqikJfUx9XY9cevx5PU0+VsKaimiKeO/EcH4/9mPC7w3nA/wGV/Z8IeoI7fe7s1thaUi2VgvJk58mE5oayJ3UPscWxHFhwgLB/hbFswLJuz3ewzWAiCiJUCrHp5emE54d3KyBorudcdqXs6tLrObMyExcjl27PC8DNwoCq+iaGOwSSWZVEbrnwnhXBgr1TjNQ11bFg1wLG/jKW8Lzwrg/4J2AXBCOfFoXGiPXgOgYsvUif8O3tnpkKEomEuR5z2Z60veud1ahi5QexO+H7iSI4bs9y+MAZvh4l3vNdy2DMCvwczPihJAjl0Edh5LPdHj6zpIaq+iYSCio5myzH0UwfZ/P2gXiWMp1Wn+fEgio8rWVg7iHyKL4YIBbik4+KnRVN8NuDMOYFGHgPXN0qiudWqt1f/57lR6BPGvO8ZpJUUElzs5Ksklo0pBLsTW5+XvUw8SCxNJGaxhqWn1jO0wOf7tJiqbu8Pu07dmi4cTHzZKtK38dGRlo3C8+XM8vwtJZh2FXgolIpCvSmQvFMbiTE7SLU2gIryTBOJckJTS3mXHJxh4cHWgTSz7wfTx97WuSb3MA7SSlMKC0jt7gMUO1akkgkjLUMYsylp1lasgpnc33S5TW4GbvR1NzE++HvszN5JyuCV3TrNav5a/NQwEMczTzKGIcx2Bvak1OVQ6OikYTSBBZ5L2LlmJW9FlCoUfN3pEHRQGldaacCDjVq1Nw++ln049Sdp9gzfw+6Gn9sF6268HyLmOmaMcd9Duti1pFRkcG22dvYl7aPNxvSWZa1mxhJk/AdlmrA3DWcLbmKr7EH5hEbcDj6Ptk3WG0omhVM3ja5S1Xrgeh8pvnbsP58Bss2XWZDaPsL5espbyznpdMv8e6od7ExsKG2QUFhRT3T/W3411An3pjph6eVjN8jWgrhhbFg5YubiRs+5j4czjjcNpihlUhi3/ciJOxHefU3lh1dxl1772Luzrl4mHi089HzMvUiyNCJ9T+PFW2iiQfAe7rw1YzbDe4hEPIqX2fsw0tnFnfZlkJlHvY5BwlURHO+yYsNx2TsTTlKaKpqkEd3efrXoygUUi6mdNKibOIIC79nkNsUfisMo/me38FhiPB2vBF5AkpLL7Irc3CQda487S7bLmWj5TFehFwlH4NqOT9N/YmxDmMB8DX3xc/cj++nfM++tH1UNVR1PlhtKaSdosayPwH2Rq12G/uv5lHciUL+18RfefTQk+SW17I+NIPaBgXV9U3E5JZja6zLJYW7CB3shJyyWsZ9fKJ1/JqGJhLzKxnubs4AJ1NOJ95E9ZwRCjVyYrT6dXmDfD3R8mhWnFpBbEksi/cuZqbbTNLK0/40n6K+RCKR8MqQVziZfZIgqyAmOE3A2sCacY7jiCiMYIDVAIbbDWdXyi6Ka4tpVjZjrNP9UDZ3E3fSytN6NKc9qXuY7ja9VzdQHiYeKornfWn7CHEMYZjtMHQ0dPpU9WWsY0yQVRDvhb3HwwEPY6Rt1OM5W+hZtC7ugLAYuWf/PTwR9AQ2Bl2H7LkZu+Eoc+Rczrmb7pdekd4rxbNEAn42tpjoGFOrLKSuUSFa6HupeI4qisLT1JPlg5ezJWFLr8boiK0JW0koae+V/T+BhhaMWAaBi+CJ8+L/gLITj/DbyRz3OexP209WxZ9r1fU/j8tomPMljFouwi2fDIdnImHMCojcBCGvQtC/cLc0RF9Phws+L4Fu12F618gqrWGUpyVhqcXsisplTpBdh/tZynRafZ4TCyrxsjYEc3c4+Jr43E3+P4j4WeycegKUChj2BHhOFirtglgVxTNAg7Kc7NpoZrhPxFhPi5yy2ha1s2mX34eepp5cKrjES6dewtfMlwWeC7r9mrtCy7ofrvN+QKJoIKflHORlLSOrvJEmRVu+R2VDZYdq4xOJhYzx7EbnSk0xSKSgbwZGdiCR0Bi3m6O1OUx3ncpHBxPwtDKktKah1Sf/eiQSCf8Z/R+8TL24/8D9LD+xnLomofwpq60ntfESw5RWfPr9Ouoa21t6VYVvIJx+2NUmMEUjgvTiaiQSCSvHrkSmLePTcZ9irtcNn2o1f3lmu8/mt9m/sWzAMuwM7cityiWuJA4nmZNayalGTQfkV+djpW+FprSLBUQ1atTcFnQ1ddHT7Jn4rzeoC899wFDbodQr6pnjPgdbQ1t+nPIj2gZWaEqk7LBxFb7ILRzNOMoknztIu+sUOmW1ZJWnq3g1ZldlU15fzqGMQzd9zoMx+Twyxo1Z/e1YMdWH6NxyGpqaeXNnNMt/iSSpoJKskhrmrznL6JXH+DzhJ2a5zWoNh0kpqsLF3ADN61LCl45wYV1ohphPYZxQBwHjHccTmhuqOgH/BSJoZ8F3XDn+FinlKZxYdILXh77OXT53te1XVwFNDZB7mWfjTrFes5HyrfeCtiFYeIBNAGXaeih9ZpGgo0uksgZ57kCm1eyCUc8hMbbHSlLO/ftqCLJ1xkrXkQ9O7Onxe5RVUkNkUSTDHQaRWXITRXm/eTwycBnyWjnfXPkGAhZC9G/t9ytKoMjMBX0tfQy02iuaekJBRR2XM0vxmrUc7vhRFOET9mGsY9x6w6ijocP66esZYDWAYbbD2Je2T3WQpgZoarlpC/8OvKfTpG9NgL0x0TnllNU08NzWSLZfzlE5LL86n3dC3+GHqz+QXJaEt3MxWy9mMe3zU7yxI5qY3AruG+HCoTJ7mjsqPKeehCu/svF8BopmJenFQo15LrmYAAdjdLU0mB1kx7aInPbHglApHXuX3YEzyCirx74Hiue4kjhG24/mg9EfsGPODl4MfhF7Q3uya//8wM6+wN3EnW2zt/Fi8Itoa2gDsLTfUgZaDcTGwIZnBj7DxriNPH7kceZ5zOtRcdXF2KVDFVdnNDY3cjjjMNNdp/f4dYBYaLpe8bw7ZTez3Gb1aqzuMM11Gtb61oQ4hvR6jMf7P8530d/RoGjgRNYJ5nnM427fu7t9/CTnSRzrwg84syITZ6PuezyDKDzbGumiq6VBoGUgJmZZJBdWkVFcQz+77i8+XM+F/AsMsRnCFJcphOaGUl7fCy/4G8iryuOD8A/+NJ+wPxQjW5WQ2r8atoa2LPFbwpL9S9iasLV1+6WCS51aBfSErpT7fzWUSuXNF2OvoaEJnpPAd6b4v7SlUOk3Gx7YL873EgkSiYQ5QfbsjOzkvNUBdY0KSmsamd3fjm9Pp3E8vpAZgR37+VrJdCmqEO9TUmGl8F628BLF5yGPinmkn4bKAtER5jtbXEfa9ofKfGHlZty2gFXZUMnjRx5nab+lyLRleNvIiM2r4FKGKDx3RT/zfjgbOeNp6snrw17vc7WmxMCc/hJdohLEwp6BjiZm+hqkX+e3/OqZV9kct7ndscfiCpnga931k5SkgplbyxNKwDaIvY5+OBo7M79/IMmFVYz1tmK4uzlnO1E9a0o1eX7w8xxYcAANiQavn30dgO/CjqOnqc+IoNncrXGEI5s/V7FoaS7Ppfzkl+T4PoROyIsMrzosroUaaxlkPYhlA5YRZBXU3V+Xmv8hrhWeo4qiCLTs2o5HjZp/IrnVud3u0lSjRs3fF3XhuQ/QlGqycsxKlvZbCoCJrgmvD3+DZUFPcay5gmZlm6ojojACGy1/7lx7nr06i9BsrCdRnt76eGJpIua65hxKP8TB9IMcSDvQ7vnS5NVU1TfR38GEd+b4s3SEC46m+my/nM3h2AKsjXV54dco3tsbR7CrGe/O8Se6Mgpr2ooyKUVVeFirWk4MczNDKoHzKcUthWcRKjbcdjjnc8+rhhmNfx0GLQWHYLZr1jPfZRqaUk2muk5llvt1BaZ9L8LmxXDgVRzHvYGHVSAxkgYRYAeUN9UwzdGBA3pafJ2wmaW1ShRFGVjkHoPAxRBwBxKnYSwe6sIbM/2Y6TmWtKqr7WwdumLtmSuY2IQxxnEYKKG8pvMbax0NHT4c/SEb4zZS6z5eWExkhokbjcY6OPkR5ESQZWTZJzYbOy7nMM3fFj1tDbHBdxbE7BABVx0wyXkSp3NOo1Qq2Z60nWZlMzX7lvPh7wt45eQKCP9GBOIAIz0sOJlYxP/ticPcQIeTNyiPnzn+DFpSLTbN2ISHzgx0zE+zeIgT941w4XBcARczShjpYUGVeQCK7MuqfpgAF79Huespzl0Ip7+DMely8b7siMxhdn+h9poeYEtYWnFra7EKyUdpqMrjtax9XJKfxMG0+2qRpNIkvEyFpYuzkTN6mnr4mPuQVtMzZe9fGS9TL36eJpRvdoZ2vDn8TYbaDuXF4Bd7NI6LkQvpFend3j80NxRHmWOv1fxuJm5kVGTQ2NxIXHEc5Q3lDLYZ3KuxusMst1lsmrEJDalGr8cIsAzA1ciVg+kHu22xcT3jncZzIutEa4jWjcVcea2cZmUz5ro9U70NcTXj6Qki7HOqy1QwjGBzeCYDnEzQ1uzdKfyad7WRthEj7UdyMP1g1wd1wTdXvmGIzRDO5Jy55bFuhdyqXJ448gSFNYW3dR5/NI/3f5yVY1byW6JYGI2Rx/DAwQfYlti7INFrHMk4wp177vyfKj5vTdjKuK3jWBO5ps3O6xaZ3d+OfVfzVLIvbkZ2aQ32JnpM8LFiZn9b9j8zptXX+UasjHSIzavgSEollzPLROE5+CFYulsUxHVk4L8Qzn4OiQfBa4o4UKoBLqPAykcUzVv49uq3eJl68WjgowCM9rTkYHS+UDw7d114lmnL+CzkM54e+PQfptjsb+pLVFZbSLGrqTbx+cIGTF4r53T2aeJL41WOySyuobSmgUD7biywFSUIu5IWLnqO4r+aNfx72L9xtTBgqKsZk/2sGelhwdlkeafDpMmrqa2X8PaItzmXew55rZxdKfsZbTcR+s2jn7U+AalryTr+fcskz6NcM5wTBLNo/p3gFoJ92UVKspPhQxfYt0KIAuoq4MJ3GGaf7P4vTc1fHht9G4pqi9gSv6XDMHE1atSI6zI7w447gNSoUfPPQV147iOG2A5RSWiNzinHNfhx9LVlxMhjACiuLaaioYIfTlbx+Dh3nnnyOcbUNrLm1A+txyWUJDDPcx4FNQW8evpVfktqr7Y9lyJnpLsFUmmbKmWgswkfHUxgzgB7XpzsjaaGlCvZZTw30Qt7qwrMdPT59EBRq4InqaAKD0vVwrNEImHhIAdOXowUad76IszFQeaAplSzw1b9WkUdh/X1mC3z7PgXU5wMZRlQVw4D7sHTzJuk4HtbfRM3xm3E1cyLDyL+S2RRJCOanXje/DwSc3dh5zHkEbTu+I63ZvdDV0uD/lYBWJoX8f2ZNNVC+E2oqm9iR/7/Md55OIu8F+Fgpn9z1TNCURZoEcihvLMw7UP4/WE48DKEfSUKu5EbyNLRv+XCs1KpZFtENgsGXVfg85ws1DsfukJU+zb4YJtgLuVfIrIokn+f+zeXss/wQfZh0srTOJ11AqWFJ1h6A+Bgqs8bM33ZFpHNx3f0JyKjlLLaGmKKY8iryiOvKo8Xg1/EVNeU+rJBVDZnE1r3Mv09yxjpbkFeWR2e1oY4ObtRL9GB0jQampr58ngyjY2NkHaKULO5/J/OBsZ5W5FeXE1lXSMnE4qYESDUXoYRa3nYpZADYVch5jpfUkUjjfte4mOd+UgkUpLrdzPGo3thcCAWabzMVL3EfUz/XoXnGxnvNJ7nBz/fY6sKK30rqhuru6cKRFhjzHCb0ZspAqCnqYe9oT2/Jf7G62df5+GAh//QUCWJRIJM+9YVqgu9FvJLwi/EFMe0hX92EweZA5Z6lqy9upal+5cy9pexvHXurdZ27YSSBLzNvHusJjTR12bxEKFuHO0wmgZpDr9cvsK9w3umnL5GbVMtcSVxBFkKBd501+kcSG+/wNld6prqeP3M61zIv8D7o9+noqGCnKruK0X7koqGCu7Zfw+p5amc/AcUeAZbD0ZeKye2OJY3zr3BHV53sCFuAwklCZzKPtX1AB1w6P/Zu+/4Gs/3geOfk71k78gWkpgRI3bEprSKUvpV39KWLkVb1e+vw6ru3dJqS6uUqmqpVaPUXhErEYSE7CER2ev+/XHqcJogiRDqer9eXi95xv3c9zlPnjznOtdz3fF/kJyXzPfHv2djwkZS8lIAbRb0/tT9ddn9OpGan8rnUZ/zcfePOXvxLPf/ev8NS5VVh6e9BaPaezNs3q5qTeJ8/kIhDe3MsbM0YVq/IFxtzJh7eC5HMo5U2tbL3oJfIhPZn1jA4BAPGjlbgYmF7p4LgPBpELVYO8mg81VlNQLv0waf/1ZUVsSvp35lfIvxumvLA63c2RiTxpmMfJp5VL9UyK3U0q8PUZfidT/72Jlw/O/5J9aeWYufrZ/eUzIAm0+k0b2Js9697jWlHtFNAKmU4q20HbzWaRZN7LX3QkufCKOZhw2d/LWB56ruH7/ZcZbBX+yk09tbWHUokwjPCD7c/xnZmr1MaPMQOAdh+p9lnOn2KVbbZ1KSkwr7v+ZowFPs9p+IgaEBWLthaOPKw5kfUxAwEJIOaMvJRf8K+77G/B/BdXF3MzY0pq1rW8Y1H0dvn9713R0h7khJeUkSeBZCSOD5VigqLWfQZzvYGJ1Gb5/evLLjFb4++jVHM4/iaxXEydR8Rrb3AgMDelj4cvDCNkrKtFnRJ7NPEmQfxAttXuCNsHc4lnFcL2MaYO+ZC4T56WfNhblUMK5oIfe3csfAQMMnD4fw1eg2mBkbsjd1L63smrF4XHtm/h7N1th0TqfnEeBSeZK9fs3cSDp5kAqnIN0yjUZDmHsYe1IqTy53LPMYPkZWOF9MqfrFyI6H/6yEMb+DgaH2EfzSi2DpQHpBOktPLOWtLm/RtWFXxrcYz8FcN7peWgO+2trGGBrrfRgLdgimyCCBXXGZvLLy6HXfh+z8EmIzivhq1x5MTHN5tdOLGGgM8LI31ws8F5WW602od9nQxkNZfnI5tBrJxgEzWJywnrK/3oPHNnB64Hv8nnemxvVaAQ6fz+H5pYdYezSF8xcKySkopa3PVVlJZtbaupOPb9bWfLyoH8RxNHfExdKFjyM/xs3Sja/3vccmSzPmlFhiWFpIRmA/ve0HhzRkw/Nd6eDvQDMPG748sJL/rv8vn+5fRBePLrqaW6dSS1jUZwXPhjzL838+T+emJTR1t8bUyJBAV2vOmjSGpEjmrIvh3Q2x7N+zjUtGdkzPH0LTilgCLXOJzyrgj+NptPezx87SBPLSYescJqS9QaedY1C/PaPN5o5aAosGk4ojC9LNMCwOwMq8gtOXKn9Ir4pSSht4tvtH4NkhkISC6peUuFcYaAzwtvauVrmNgtIC/jr/F729b+5D1Lvd3mVxzGL8bf0Z1njYTbV1u4R7hhOfG0+AbUCtSugM8h/EjqQdPBz4MNuGbyPxUiIrT2u/bInNjqWJXZOb6p+JoQk+Zh2wdTlIr+Ab156+WuKlRIrLi9mdvJtmjs10mY0d3TsSkxXDhaILNe5PYVkhz2x+huLyYpYPXI6dmR0d3TuyM+nKRJbpBel1Uv6hOg6kHsDfxp+nWz2t14d/K0MDQ/r49GHM+jG0cGzB/9r/D1tTW8asH8OrO1+tcdZyaXkpO5J38FH3j5h3eB4/nviRoauH8uyWZ3lk7SOM3TCW5LwbT2R8O30f/T0PBDxAJ49OvNvtXfr69OXnkz9zOvs0b+97+6bafqFPE3wdrarMkD2Xe47tidonjwrLCjl3oQAbm3Qi0yIBbaB+UfSiKp8mGNHWk0Ov9WZaNxde6huIsWEVt+JWTtBrBrQerVeujVYPQ883dD+uj19PU8emeFpf+RLcwcqUDn4OBLo1wNSo9k+B1KWmTR4gTZUSf0EbXA51t+CHfdG0+eoRvjzyFZNDJ5OQm8D5S+fp8VMPvjn6DdtOphHexLl6B0g5Aq7awPOOpB1UUEGEV4Ru9eWgvLeDBcaGBsRl6H8JezLtEvO2xfH7s51Z+kQYH206xQDfQaw6uwI/o8E0srvyRV94eE/22fTl5PwxqJMbWEtnWnleqbdv4N+DLhxitdUwaP4QnN2mLUvW4Skymj9Zq9dP3Lm+7PUlgwMG13c3hLhjJedJqQ0hhASeb4mErAKMDAx4d0MszcyH0tjwv3wRuYDpf35H7Dk7Hu/qp/sw0M2nC2VGl/j5cBTJOYXsOn+Uhpb+9PMZwLcbzSgoMiY684yubaUUe89m0d7PXu+YnY1ieMJoDYHW2onVDmT+wfTIcXxw8AM2n9tM0wZNaezSgHmPhDLlp8P8GZuuzbL5B097C8JME0gx189g7uDWgV3JlSfOOp55nGZWXpBRRRZH8SVtuQgbT13wOMAugJPZJ8kqzGLshrE82vRRvKy9mNlpJr6mvThr5ItR6SXwC6/ytXW2cMbE0Jh5Y/z4/XAK2dd4DDa/uIxHF+xj2h8pLIhaRWf3cF3GpdffGc+XJ7Z5d0Ms4747UKmNrg27klmYyb6UfbwV9SlrXP14rVk3cPBn0rlVtHBuVaMasAA5BSX8d+F+DAw0LN1/nj1nsgjzc6g6C9K1OYRNgOWPQs452PGRdoZ7oJ1rOw6mHWR249Hsu3SWPs5tsAscSKOSYk65NK7UVBNXbTZo76aurD+zFQtDG1afW8SJM56sPJTIr4eSKC4tx8PWgh7ePZjadiqLzrzBl48GAhDo1oDIcl/ST+xiw7FUXrsvmIT9a1iXH8ic4W0xCBpI8+zNxGfmszE6jX7N/q5teWABNB2McZ9Z7LKIYHenb2DFOG3guc1jTDN6if6hJpQXOzC40eDKtauvIa0gDRMDE+zN9H8PQp1DGeJedxMj/Zt4W3tzNvfG2eCbzm2ipXPLm54IqbFdY1YOWsmcznPumhneTQxNGBowlG6e3Wq1/+imo1ncfzF9fftiY2rDmGZjWHV6FaDNeP7nFyW1MaHVE5jZHWJR9HdsPb+1Wk9+FJQWMHrdaL468hXrzq7Tluz4m5mRGR3dO/LnuT9r3JefYn/CzMiMt7q8pQtk9/LuxdLYpZRWlKKU4smNT/Ll4S9r3HZtRKZFEuoSSgf3DuxL2XdXlYuorZGBI5nYeiKvd3gdjUbD7M6zWXbfMnysfWqU9VxYVshfSX/ha+NLmFsYu0fu5ts+37Jm8Br6+/ZndPBoBgcM1p9ouJ4ppdhybgsDfK88nTHIfxBrz65l7uG5/BDzw01n3/s5WZJysajS8hWnVjBp6yS6/9SdDks6sDzhHfYVvs2krZNIy08jKj0KpRS7U3ZX2lej0WBYnSze0EchfOo1Vyul+PHEj/rzavzt2YgAHu/id+Nj3CYmpg0YUmrEj0fmAxDkZEqvrjvwsHbEt+QlOrl3ws3SjflH5hPoEMjas+s4mPEnHfyr8XeoogLSjmnvmYDvjn/Hf5v+t8q/OxqNho5X1XlOvVhEZl4xvx5K4sEQDxraWdDMw4YAFysSkpxpzNM82mxkpTa6PfE+jkXxnLZqw+4UpRd4Jmgg2Q178u1Jc5RfN4jbqp0U0rd2f1eEEOJulpyXjJtl1XMeCCHuHfUeeK6oqOC1115j+PDh/Oc//yEhQT8jb8uWLQwZMoThw4fz008/VWuf+nYmI4+ujZ1wtjblo81naGjejC4u95OpDjChQwT/7eij29bIPYS+xWasOLuAPfFJFFXkMmNlGs8vi8LSxAhXswCe+2UVzy89xIX8EuKzCjDQaPCy16/D55B9BAMUnN1GWUUZ8w7PY0STEaC0j6I2t9bekLfxseevl7rz4fBWNHau+tH0cNNYfsvR/8DSwb0DB9IO6B4bv+xY1jGaObXU1oT+p+wEsPPWy9ZpZNuIsxfPMvfwXMLcwhjbfKxu3cGEbBz8QsDAGLw6XPP1DXYI5kxuLK28bNkfX3WW3tQVR2ji0oD5gz1xdj3Jw02vfDD1srcgLiOP7u9vZerPR1gRmUhmXnGlDBgjAyOeaPEEk7dNxs/Gj/d6fs6uwmQulVwirSCNp1s9jbVJzR5jnbs1jr7NXHljUFMOxl9g28mMSl8i6Ok8WRu4/zQUDi6AnR8B2izFRta+ChP9aAAAIABJREFUtFnzClO9BjCu83RoNZIA55acKkgF4PfU35l7eK5ecw+3a0hWxTHyz4/Cx7IlrRzbszU2g1+jkhjY0l33Qa2/X3/6+vZlxt7XAGji0oCtlzwpSjjAkNCGPNy2IS0ubiXDpbN28qIWD+GasJqzmfnsjMskvImTdsLDA99A+/HQ6mHs+07j4xM28MJJGPM7F3zv43B6GU62eUwO78jDze5j87nNlFWU6fr79r63q6wZW1W2M2gfe2xhIxO8VMXH2of4i/HX3UYpxaLoRYwMHHnd7arL2ND4puou14eJrSfyePPH66StMLcw0grSOJNzhpPZJwm0D7zpNns3CeKbvvOIyohi5u6Z7Evdd81tlVKczz3PF1Ff4Gfjx0+xP7EzaSe9vHvpbdfLuxebzm2qcV+OZR6jj08fvfe4u2d3HM0cWRS9iO1J2ykoLWDFqRW3Jes5Mj2S1i6tcTR3pGGDhuxLufZr82/hae3JqKBRumu3r40vXtZeDA4YzMpT2mz7gtKCG77+j6x9hP/t+J8uiHv5SRhbM1v6+fZjgN8Aenv35o+EP/gr8S9isvT/5leoimqXv6orp3NOo5TS+1vQyK4RNqY27E7ZzSD/Qaw8tZIPD35YZamw6nCxNiO1isDz6ZzTvNn5TRb2Xci24dsoLjFllN/LPBz4MC/99RLrzq7j4cCHSc1PJSYrRvde1KWjmUe5WHyRTu6dKq1r3tCG/s3vrA/6D1kF8HviVnJLctmXvY+4nDgWDX6T+FQrolNyaWzXmFVxqxjeZDh9PUZj4rATOwvj6zcavQqSI7XJDRb2ZBRkEH0hmj4+fa65S6dGjuz4O4t9yvIoHvl6L79FJXN/qysZeRN7BDBn3UmOnvImIrDy5IZmFlbwyAomXhjCqfRLNLu6DrV3B2we+5m84jKiS92gvERbvs6uduWRhBDiblWhKkjITZCMZyFE/QeeN23aRElJCcuWLWPKlCm89dZbunWlpaXMmTOHb7/9lkWLFrFs2TIyMjKuu099Scst4p31J1h3NIUzmfn4O1uyeFwYvz3diUm9GjOj+5N4NvDkweAO+vXq3FryXHYq5/KP8Gn0C3iZtaaDnxNBbtZ8NKIVDzbtgJ/HBQwMNLz62zG+2xVPh6oyZBP3a2v/xW1h07lNOFk4MaTxECa3mczaB9dia3IlG8PS1Ij+zd2qrptXWoR7fgxrL/rozehuY2pDE7smlYIcxzOP09SrW9UZzzkJYOejt8jS2BIHcwd+O/0bj7fQD+6cSr+ErXcLGPuHtt7hNQQ7BHM86zjtfe2rDDz/fiSZmJRcZj7QDGPjAgpVut7EZp72FqyKSsbNxpzU3CLGd/NnQHM31h6pXC5koP9AnMydGNd8HG6WbigUW89vpbFd4xoF1PaeyaLpa+tZduA8E3sEYG1mTFN3G9YeS6lUNkWPgQEMngcTdsGjv8OeLyArjm6e3VjsOwKNdwdGRLyFR4OG4OBPQNMRnMrRPsp68tJJVset1gsGxF86hbOFHS2cmvLbg4t4dUAIH48IYeF/2zF7cHO9Qz8T8gxnLp7hYNpBLE2NyGgQhMOlWHo2ccD85G80tDPngYfGaDf27oRhcQ7NNadp5GyFg5Wptp6zUyC4aOtTdm/izNGkixQYaYP1m2PSCPN3ICn/PD423ng28MTFwoWDaQd1fdietJ1fTv2i169zued478B7tc5KvVcF2gfyV+JflJaXcj73fKUSPukF6fx+5ndKykvo5FE5kHGv0Gg0dZahbWRgxH3+9/HhwQ85f+k8/rb+ddJuE/smfNT9I55s+SRLYpZUWp+Ul8TxzONM2zGNR9Y9wq6UXczqPIsO7h1o6dxSbz4C0M5RcDjjcI0Dh8ezjtPUoaneMo1Gw6thr7IkZgkvb3+Z51o/R6B9YJ1MYHg9BaUFnM45TXNH7XXsseaP8fL2l/kh+odbetw7VW/v3pzMPsmyE8sYuWYkz25+Vu9LvaulF6STVpDGzhE7GRl07S+d2rm1I/5iPJP+nMT30d/rliulmPrXVObsm1Pn47ieree3Eu4ZXun3dXTwaCa0nMDIoJF8deQrlsUu05W8+SelFJmF155szs3GjJSLlScsPJ19mkD7QHxsfLA2sebCuf4MbdqDx5s/jq+NL8tPLqeHVw/aurTlsQ2PMWPPDLKLsm9uwP+w9MRSRjQZcdd8uefqFExPi4Z8fuhzliYu5cW2L2JtakGwmzUJWQU0tmuMhZEFHdw6UJDdGGOTIqIyoq7dYFYcLB8Dyx7RldnYdG4T3Rp2w8TQ5Jq7dWzkwN4zWWyMTuNUWh6Brg2wNDUkyO1KMkYbH3sO/l9Ptr4Qjq1F1W25+jalR4e2BDg3wMxY/z0wMNAwOMSDlYeStU/w+cn9ihDi3rM7eTeO5o6S8SyEqP/A88GDB+nSpQsArVq14tixY7p1cXFxeHl5YWNjg4mJCaGhoRw4cOC6+9xOWXnFrD+mzS59ZkkkhxNz+HbnWeLS8/B31C9jYWNqw5rBayp94KeBGw00BnjnDcK9tAkv2fVmYs8Anu7eCEcrU0JcmlNskMDsB5oTk5xLdEouL/f7R9ZcWTGkHYcuk+H0ZhZHfs6j/rWsN5Z0AI1zIG+N7MT01dFM/imK9Fxttk83z256j+7mFOWQU5yDj0d7KCmAgn8EgbPjKwWeQVtuo49PH5wt9Gv3nU7Po5GLNXhcf1KvMLcw1p1dRwtPC/advcDFwlKKSsu13c8p5I1Vx3lvWEvMjA1JLkrG19ZXl70F2oznkvIKnu8ZwHePtWN8N3/6N3djzdGUSoEXYwNjVgxaQXu39mg0Gpo6NGX5yeUEOwRTEysPJfFsjwD2/68nLtbame67NXHCwdIUP8cb1JI1MgXHALD1hObDdBP0WZzZBgH6mYsBdgG6CXrOF54noyCD2OxYQDtz/GeHPqOXbze+GdP2hhP2GBsY80SLJ5gbNRelFA3d3MnU2NPc6Dxsno7NA+/gYWdJaXkpS2KX8X8+gYwwX0WPQGdQSjsRY9gEXXvmJoYEu1lz6FyOboLC0R28OX/pvG6SxoH+A5m+ezrr49dTWFZIan4qu5N3k1+aD8D+1P2MXjeaUYGjalzm5F4X4RWBi4ULo9aO4r5f7+Ojgx/p1hWUFjBk1RC+OfoNz4U8d0snArzXPN3qadBoS51cLxhSG/f53cfB9IOVyglM3jqZl7e/jJHGiPVD1vPLoF9wtXTl/8L+j+kdp1dqx97MHhMDE9IK0qp97IvFF7lQdAFv68pZfJ7Wnqx5cA3TO06nj08fhjYeyqq4VTUfYDUl5SXx1ZGvaGLXBDMj7fW1r09flt63lLmH59Z5wO9uYGFswacRn/Jh5Id08uhEBRV8EfUFoH3vrrY3ZS9tXdreMIBpbGDMJxGfsLDvQvam7KWwrJCX/nqJt/a9xansU2yI38Dp7NO3bExX25W8i8Ux2rI2/3R/o/v5T/B/CLYPZlbnWczrOY/NCZur/GJlffx6hqwaossIzy3J1at37mpjRsrFfArLrgSf80ryyC7O1mVwxWcVYGJkgIetOYYGhrze4XUW9l1IsEMw/f36M8h/ED28etRpmZK4nDh2JO3ggUYP1Fmbt5xjYyYqO34/8zsNjBroMrUvlz/r6N6RMc3GYGxozJ4zOfTzeoj5f5fmqNJf70Hn58HCEdxDAO3kmDean8C5gRlTejfhiUUH+E+YN+8Oa8kP49pX+gLDyNAAd1vz67b1XI8A5o9uU+W6wa09WHU4mfJuL0PXFymvUMRmVs6eF0KIf5MjGUfIKtSWM1p6YikPBz5815TcE0LcOvUeXcjLy8PK6kqQ1tDQkLKyMt26Bg2uZCBYWlqSl5d33X1uh/XHUtlzJotnlhxi8k9RZFwqJjo5ly9GhRKdnEtUYg7+zpWDiVVedDUa8GjNN+nv80X8ErpEToa8DN3qYIdgYrNjMTZSrH62Mz8+Hobz34FLndRjYO8P7q0549KYxNzzdPt1ChxbUfPBxe8An84087Bh64vhuNmY0e/j7cxZF4OfRTs2xG/QTdp0NPMoQQ5BGBgYgld77QQqV7tG4PnFNi/yQpsX9JZVVCht4LmKutP/FOoSSjPHZmzP+p6TWQmEv/sn/T7ezi+RiTy1OJJxXfwI8dIG+JOLkvGx1u+Dt4Ml7w5tQYerMo3b+NhTXFbBnjPaD5xXf0C9OgjXzLEZh9IPEWR/ZfLFGymvUGyKSaN/Mze9SYQGtXRncq/GNftj3KQfxK7T1jQ8vQka6QeeL5cyKSgtIL04nQcDHuS307+xK3kXw1YPo7FdY55r/Vy1DzfQfyC5Jbm8vf9tmnpYkWPXDINNr3HO1h18OgOw5MQS1pxZg4N7Gz5zTCTCIx1SDmu/iAjQf9y1tY8le89ksXhvAl4OlnTwtyMlLwWPBtoP8I8EPcKrYa8yY/cMYi/E4mPtQ6hLKH+e/5OS8hIm/jmROV3mMDxwePVfMwFoz+NZnWfR3bM7vz/wO9sStzFrzywulVxiVdwqQl1C+fWBX+nh3aO+u/qvYmpoyofhH/Jlr7qvc2xhbEFPr55sTtisW5aan0pSXhK/3P8LszrPwtzoSuDE2sS60hd+l12uv19d0VnRBNoHXjNYaWpoSi/vXhgZGNHZozPHM49XCnjWlWnbp5GUl8Sk0El6yz2sPOjl3YsfYu7NrOcm9k3YMGQDL7R5gbe6vMXyk8s5mHaQfr/0Y3/qft12e1P20t6tfbXavPz319jAmC+iviDxUiJZRVm83fVtHm/+OLP3zr5mZnVduVB0gRe2vsA7Xd8hxDnkmttpNBoG+Q+ipVNLyirKdE8DXW3FyRWUq3LWn11PaXkpEzZO4Pk/n0cpRWlFKa7Wppwz+pJZe2bp9om7GIevja/u3D8Qf4FQbzu944a6hKLRaOjj04dp7afRz7cf686uq5PxK6V4c++bPNnySWzNbG+8w53CsTH2F87yfvj7POH7hO7ex8tBG3hu7tScJ1o8AcCJ1FzGtXyYk9knOZpRxUTSadFwagN0fA4eWw8dniG3JJeYCzF09Oh4w6482tGHDx5qyeiOPhgbGuDcwOyG+1TF2NAAV5uq9/V3ssLT3oLvYuB8hSMPf7WHH6LuvS/BhBD3juyibCZsmsCrO1/lWOYxojKi6O/Xv767JYS4E6h69uabb6o1a9bofu7SpYvu/zExMWrcuHG6n2fPnq3WrVt33X2uZcOGDQrQ/Vu+fLlavny53rKnnnpKRUdHKycnJ92y4OBgFR0drYYNG6a3bciLi1XY4zP1lr3xxhtqyCeb9ZaFh4er6OhoFR4errc8OjpavfHGG3rL3Ie8oo58MERv2bBhw1Tvpb1Vo8BGumVOTk7q3MZ5atKovpXG9N9P/6u37JknxqiETV8rR0fHG45p69at6qfxzSqNad2uKL1l9iH26s1Nbyqn1k56y7MXjFCzp4zVW/bNjKfU1q1bK40pOjpaBQcH65Y5ODqp1m+sU0899VS13qcdUTuUmZ2Zbpmnf6B6+PMtKiRikP623z+lHpn5SKUxRUdHV3qfPvxtj3IODrvh++Q10UstWrfohmNycnJSv2yLVH1HjKvTc+/cS67qu/+NvOGYHFs7qj8O/lHpfapqTJ9//vk136fAoEDdMms7c3VstqNyul+/zU8XfVppTC8Nbl1pTFY+DVSnN9cru9b65+7WrVvV559/rres+zPd1YTVE/SWuYa6Vvv36Xpjuvp9io6OrvF1q66vJ1WN/1rnaV2P38TWRHVb0k0NGzus2mOq7vWkvsbk5OSkoqOjq309qe77tGHDhjtuTK99/Zrq/15/vWWhD4fW+bnXpUuXmxpT496N1dy/5tb5+/Rn5J/KpbvLDcf06uuv3pb36U6+nszcOLPS+I8fP67sQ+xrNX67bnZqya4leuM3tzNX09ZPq9F7Wp3ryYbNN/e799K6l1T/ifq/J7M+nKXafNpGb1lArwDVf1l/5ezvrFtmZGOiwhaFqfETxtf6fTp87LBy7u6sP6abvJ4cPX70jrtG3uz1ZMe2zWrWhAf0lgVNCVJP/aDfz/92dFHJq2brjcnO0U4NXj643sZ0rfcp5I11ymPAs7V6n2pzPVFKqXXr1t1Rr8Ht/HseGRn5rxvTv/F9kjHVz5hqc02pyfXE0t5St8zCx0J9t/O7O2r8cj25O85TGdPdMaaaXk/qPfC8fv16NXXqVKWUUocOHVJjx47VrSspKVG9evVS2dnZqri4WA0ePFilpqZed59riY6OrvUN3GUVFRXqgc93qMV7ElRZeYUqLStX8/+KUz4v/67mbT2tlFLqm+1nVMiMP2rc9rC5u1T72ZuUSo9V6m0/pUqLdOte/utlteLkCqWUUruTd6vvN7+k1PtBSn0/WKno1UqVlShVUaEOpR1SnX/srM7mnNXuuOF/Sr3lrdQbturcpvk37kRJoVKz3JQqyq1y9X8X7FNfbjutki4lqX4r+qnXdr6mKioqtCuzE5R621epz9orNa+rdtknrZVKPV6t8W85kaZGzd9TrW2v9v3x79W0v6Zdc/1jvz2m1p1dV622ikvLVae3Nqthc3epR7/dW+U2WYVZqtOPnVRJeckN26uoqFDtZ29SbWdtVJ//eapafaiW5f9Vao6nUuf2Vbl6btRc1Wt5L/XYb4/V3TGVUudzz6uuSzqpBxaGqF7Le6m5UXPV/CPz1QtbX9Bts2rnm2rSt22UWjBAqdj1uuULjy1Ug38brHr+1FP5vzFffb87Ximl1IqTK9TkPydXOtYHBz5QId+HqG+PfqvySvJU2OIw9frO19Vnhz67YT9r+ntem+tCXVxP7hSH0g6p9/a/d+V3uRr+LWOvqTtx3DlFOar94vaquKxYKaXU+I3jq33Nu9rKUyvVS9teUiXlJVWeC1ePPS0/TQ1cOVBtit9U7fZXnV6lnt38bI37dSNfRH2h3tzz5nW3Gb12tNqSsKVW7f+brif5Jfnqk8hP1IazG9STG59USil1LOOY6vNznxr9/iul1Noza1X/Ff1VeUW53vLswmzV5ccu6lzuuWq3FR0drSoqKlROUU6ldT/G/KiaL2yuvj7ytW7Z2A1j1cb4jTXq77ncc6rTj51UVmGWUkr7WozbME59eOBDVV5Rrt7Z94767th36lLxJbUjcYfq83MfdST9iPrm6Deq7duL1YO/PqQ+O/SZarawmWq/uL1acHSBru0e729VRxMr9/2flsQsUaPXjlYVFRWqsLRQ/bj7R1VSVqK6L+uuZu+ZXa1x5JXkqYifIlRkWmSNxn/HeMdfqYtJeuf76fRLqus7W9TKtWtVznQvVT7DUY36aLVufVx2nHryjyfV9F3TtedpzBqlvgxXqlz/3FsSs0S9vvP12zWSajuVdknFpmrvq2/H9UQppSIj79Lzow7ciX+nb4d7ddxKydhv5fZK1ex6Mi9qnsopylGns0+rHYk7anysO829em7dq+NW6s4ae0lJiTpz5ozuc8Kt/nf48OE62/7MmTOqpEQ/Xnal8G096dWrFzt37mTEiBHaxwfffJPVq1dTUFDA8OHDefnllxk7dixKKYYMGYKLi0uV+9wOh87nkFNQyoi2nn/Xx9XQK9iFWWti6NTIEYC+zVzJLiipcdv+zpaYGBmAU2OwdNJO1ufWEtCWdziWeYwHAx5k/dn1rDy/FpeQBzlk2YBnG0VgYWhMUl4Sz2x5hjc7v4mPjY+20W5TwSEAKsqwjfoVeoy7ficS94NzEJg2qHL1i32a8NjC/TzRtQcr71+JsYHxlTIRtl7QwE07c3fqMbiYBHnp4Ni4yraulnGpmEMJ2dUqs/FPA/wGMDdqLpdKLtHApHK/U4pSKpXauBYTIwP+fCGc3MJSwt/dSll5BUaG+tVo7M3s2Th0I8YGN5hpHUjIKkCh2PtKj7qtbdXjNQh/BRwbVbm6a8OufB71OR1tbvy4aU00bNCQ/3V4ja+Pfs3MNlOYvXc2F4svsqDvAt02IUEP8eGJH1DJkWi8tcePvRDLN0e/4cf7fuSH6B8oc8/nkfZeAGw+t5kBvgMqHauje0e+PfYtje0aY2lsSQf3Dvxy6he+7/d9pW3FzWnl3IpWzq3quxuilmxMbfCz8SMqPQpDA0Ois6J5p+s7NW4nwC6Ar49+zbBVwxgZNJKHmjwEwHv732NMszHsu7CPo7FHeTDgQcZuGMsg/0FEeEVUu/2uDbsye+9sCkoLsDC+9uSxNaGUYs2ZNczpfP1J7S6XOeju1b1Ojnu3sjC24NmQZ8kqzOKN3W9QoSpYeXolDzR6oMZ/o/r69KWje8dK9eBtzWwZ4DeAladW1qis05/n/2TmnpmsGbxGd36UVZTx3fHveK3Da3x66FP6+/Ynuzib6MxoOkd0rlF/PRt40s+nH9O2T8PR3JH9qftp79aeZ0KewUBjwIttX9Rt28mjE+seXIdGo6G5U3NWbdtJc7tOzDs8j1mdZhGZHkmYexgAxWXlnMsqoIlr1fdNV3uo8UP8dvo3fov7jYvFF3k/9n1Ocxp7M3vWnFnDxNYTsTS+/pwPy2OXE+oSet0SI3c0jzaQsAuMrkxK6mFrTkpOEbbHFrKgoj8PuaTQw+AEcB8AfrZ+vNftPUasGcG+1H20Tz4EjXpqJ1++yqnsUwTYBdzO0VRLbe5thRDibvRkyycB7b1pXU2oLcS9KjExkQYNGuDj43Nb6qQXFhZibn79uS2qs71SiqysLBITE/H19dUtr/fAs4GBATNmzNBb5u9/5UIVERFBRETEDfepSxUVim93nmXBznhcbcx47b5gWnrasuJgIkNae+hNyubtYMlnI0MIdrMGwN3WnCm9m9T4mC0b2uJgaar9waWpdrLABu5QnEtTu0B+2/s+BIzkSPohHskr4pWkdTiZOxPkEMQg/0EczzxOa+fWdG3Y9Uqjpg0g9FEozsPij9fhUio0cK1iwOWQeEBX3/lamrg04EJ+CfnFZViamlbeoPcssGkIPz+mnVjOKwwMr3+KpVwspO9H21FKMfOBZtV5qfTYm9nT3q09P0T/wIRWE/TWVagKUotS8WrgVe32jA0NcLAyxdXGjOiUXFo0rFw/8eqaqdez92wW7X0d6v5CUUXd7KsF2QfhZO6Ep7ln3R4X6O3Tm17evahQFVwsvkgn90742fjp1nvY+oGhCYluTfE0bcD3x7/nm2Pf8GLbF/Gw8iDCK4I5++aw6Vww/jb+HEw7yNtd3q50nBDnEKxNrGlir/1d6ufbjz0pe2jmWPNzRIh/uy4eXZi1dxa5xbnM7jy7yi/hbsTfxp/zl84T4hzC99HfM7TxUJLykvgu+jtiLsQQkxmDcaIx1qbWWJta83iLx2vUvo2pDc0dm7MzeSe9vHvdeIdqOJ1zmtLy0hteF3r79ObjyI/rNOh9N3Mwd8DO1I7orGjWx6/n54E/17gNjUaDjalNleseDHiQ8RvHMyJwBF8e/pLk/GTm9px73fb2pe6joLSAuYfn4m3tTVxOHLuSd+Fj48PQxkNJK0jjod8fwsTQhOdDn9dNIlkTT7d6mqWxS3E0d+TRpo8SYBtwzb/PVy93tTbD37wzQxtnM8h/EPc3ul+37vyFQtxtzfTmb7gWQwNDXg17lac3P41Go2F4w+EsjV3Kx90/5vczv7M6bjUjAkdct41N5zYxoeWE625zRwvoCac2QtCVwLOZsSFultAqbztvGX5AQ3WMNuVH9HazMrFiZOBIVpxaQfu089Cy8ut0KvsU/Xz73fIhCCGEEELcakVFRbct6FyXNBoNDg4OZGRk6C2v98DznSi7oIQTqZeY+0hrTqRcYvwPB/lsZGvWHE1hzXNdKm1/Xwv3mz7miHZXBUcvB55Tj0FKFIFdpnBWlZC55nkSOc/zTh15evBcdibvZOmJpQzyH0RcThyNbKvOgMXUihzfATismQIPLaqUJULcFlg8FIwt4aFrZ5QaGGjwcbAkPiufpu5VfOD0/zubzCMUDiyEbi/pVlVUKI4mXaSlp34gd/aaGEZ38K5VsP6yqe2m8ui6R/Fo4MEg/0G65an5qTQwalCrQEOYnwN7zmRVGXiurr1nLtDez77W+9eWRqPh7a5vY5B5a+YO1Wg0GGoMmdFxBkEOQZXWhTg2I8rKj4ILsXx3/DsW9FmAn602OB3iHIKZoRk/n/yZyLRI2ri2wcqkcjaQiaEJG4du1L13EZ4R+PXzw8hALllC/NO45uNo7dIaA40BbV3b1qoNMyMz3u/2Pl0admH0utHsSNpBan4qfXz6kJyXzH2u93Gu4hwzd8/Uyw6tiZ5ePfkj/g+2nNtCH58+hHuG16qdyzaf20yEV8QNb8jszexp5tisToPed7tWzq0Y98c4Orl3wtWyii+kb0KAXQBNHZsyZNUQOnl0Iio9iuyibOzM7K65z4HUA0zvOJ0Ze2YQ5hZGS6eWvNbhNVo7twa0QePLAdd/ZllXl62ZLeNbjq/xfq42ZhQXmfN6l9crrYvPzMfH8fpZyldr6tiUvr59SS9I50GXB+kZ3JM2Lm0oV+X8evrX6waeMwoyOHPxDO1c29V4DHeMRr3gzzkQqJ8NP9DiGOeVP95ejVh4Mo9lVitAKe0E3EUXYcU4BljY81nhEXIy8rB10f+ySSnF6ZzTBNjeeRnPQgghhBC1cbcFnS+rqt8SxamCg5Up7w3Tlrlo0dCWnMISXl5xhMEhHnjYVj/9vNZcm8PuzyAvAzJPYrb3SzpYejL7UiKNjYwxvu9DjI0t6NawGzN3zyTxUiKnc05f97HnjBZP4bBnCmx/Ty8gDEDMauj2svYG3/v65Rl8HS05m3mNwPNlHqFwcAH4XgnS7zidyehv9/HZyBBdoP7w+RwOJmTz7tCWN35NrsPV0pWPIz7mqU1P0c+nH8aGxlSoCrac24KbmVut2uwc4MiEHw7yzY6z/Pp0J9xsava+K6XYe/btCQMZAAAgAElEQVQCT3W/xpcBt1hb17bEZMfc0mN08+xW5fLOjR9k6amf8Yn+nuGBw3VBZwAjAyOWDFgCQEJuwnXbv/oLA0MDQ3lkS4hrMDY0pr1b+5tup6d3TwDGNB3D51Gf427pTrhnOPf53cfJ2JOkWaVxZMcR+vj0qVX73b26M3vvbNws3UjOS66TwPPUtlOrtW2EVwRbzm2RwPPfJoVOYmyzsfja+N5441r4JOIT3f8nbpnI9qTtel8MA2QXZXPiwgkqSipIzEukh3cP+vj0ueZNdm0DzjfL3daM5JyiKtfFZ+Xj41D9wDPA1LZTKVNlnI49rfu9beHYghm7Z6CUoqCsoFLJja+Pfk1yXjKdPTpjbHjjUl93LDtvsHDA7MIJ4ErW88CyDST43E9zNxs2HHfFRFMG2fFg7wvHVkB5CTZ56XS3cGGieTZPF2fQjiv3BCn5KZgbmWNrVvtkASGEEEIIcWvUz138XeaJrv5snNyN1wc2vfHGdcGlqbb0RW6itlTGqQ2MC3maTUblNA8cBhbaLFoTQxN6+/Rmffz662c8A8rQRJvtfGABxPx+ZUVFOcSu1T62GP4ymFw/O9jXyZKzGflVrvthTwKjvt7D3Dh7lJktuLbQrfs1KonhbTz538pjtJrxB5//eZqvtp9hbGdfzE0Ma/DiVC3QPhA/Gz82JmwEYNr2aayKW8Uwj2G1aq9PU1eOTe/DyHbevPTzEZJzCqmoUNXeP/JcNoYGGvydavaB9N/g/kb3Y2Niw7qz6xgSMOSa23lbe+Nt7X0beyaEqI4+Pn0w0hix+dxmwtzCMDTQXqO7NezG74N/r3a5oX9ytnBmZqeZLLtvGecvnScuJ67WfTyVfYqMgoxq17rt7tmd7UnbKa0orfUx/00czR3xs/W7LZkU4Z7hbD2/VW9Z7IVYBqwcwLsH3mXa8Wm0cGyhP2/EHcTL3pKErKrve7SB55o9VaXRaCrNE+Fi6YKJoQmx2bH0XN6T09mndeuyi7KZf2Q+p7JPVQre35UCemGVuvvKz+f3E2CQQpcHn6aZhw2gAdeW2if/AA79AB2egbCneP3kQYYaOvDi9qlEpkVSVlHG/CPzeW7LczR1uE336EIIIYQQd4CMjAymTZsGwJgxYyqtj4iIoLi4WPdzXFwc//nPf67ZXkxMDJ999hkAGzduJC0trc76KoHnO1EDNzA0Bp8u0OYxaNSTFgH30d2zOx099DOSe3n3Yv3Z9STmJV6ZVPBarN1g+CJYPREWDICcc9pJXqxctFkl1XA54/mfLuSX8O6GWB7t4MM5Q0+GGn6M+js7qbCknI3RaUzp05itL4SzYkJHFu1OYNfpTP0SIzfp4aCHWXB8AavjVnM44zDf9fuOYOvgWrdnYWLE0939MTc2pM9Hf7FgV3y195279QyPd/G9Iz9E32oGGgPmdJnDB+Ef4GDuUN/dEULUkIHGgGntpxHuGY6LpYtuuUajwd7s5soH3d/ofmzNbHmg0QP8fLLmtYUv+yjyI8Y2H6sLit+Iq6UrDa0acjDtYLWPUVJewrrUdbXtovhbl4Zd2JO8hw3xG1BKUVpeyis7XmFq26n8PPBnujp2ZYBf5Ulm7xS+jpacvVbgObMA7xqU2rielk4t+eDAB+SV5rHx3Ebd8oNpB2nt0ppF/RfR2aNmkyrekRr1xDLl78BzeSlsno5Bl8mYm5vTsqEtbX3sMHYNgowYSI+B3BTwjwC/7pgYmjDQKZQ5nefw1Oan6PVzL/an7mdqu6nM6XL9SUaFEEIIIf5NDh8+TLNmzSgoKMDS8ubvR4OCgnjmmWcA+P7778nLy7vpNi+TUht3Io0GXJqBX7g2+/mRFQB83P3jSoHMUJdQ0grScLN0w9Swign//qlhG5gcA3u+0Aafywqh71vV7pqvoyVL9p6rtHzBzrP0b+5G76au9Ap2Ify9LKJTcmnqbsMf0am08rTFuYF2MiA7SxO+Gh1KQlYBVqZ1dwqGNwznSMYRZu6ZyTtd36l1Vt7VjAwN+Gp0GxbsPFtlwL0qp9PziDqfzacP36WzztcBG1Obm36MXghRf5o5NtMrl1DXhjYeyvDfhzOx9cQaTxS3P3U/cTlxfBj+YY326+Degci0SMLcwgDYlbSLtq5tr1m6IDormm2Z25jM5BodR+hzNHfkk4hPmLFnBmcvnuVC0QUaWjVkkP8gNBoNozxHEdQo6MYN1RNvBwsSswspr1AYGujfg8Vn5eNbw1Ib19LcsTkfHPyAUUGj2JRwZRLBg2kHaePSpk6OcUfw7ohp7lltObm1U8DIDEK02Td2liYsH98RouK1848YGEPQfXD5C6b2T4KDPx09OrJl2BZSC1LxsfaptzIsQgghhBD14bnnniMyMhJ3d3eWLFlCWVkZ69evp2/fvtXaf+DAgbRr147Y2Fg0Gg3vv/8+R44cYenSpdx///3ExMQwdepUFi5cyAsvvEBeXh5FRUW8+OKLtG9f8xKPcqd2p7r/M2g1Sm9RlUW6DYzo7tm9ZvVvjUyg8/MQ8T8Y/gM0H1rtXX0dLTmTkYdSV8pO5BaV8sOeBMZ389P1s0egC5tj0gFYuu88w9t66rXToqEtA1ve/KSMVzM0MGRS6CR2PbyrzoOe7rbmJOcUVmvb5QfOMzTUs05KiAghxL+Ru5U7zRybsSF+Q433/fLwl4xvOR4TQ5Ma7RfsEEx0VjSgDSqP3zSev5L+uub2x7OO42fpd831ovrauLbh2z7f8supX9iVvItZnWfdNU8EmRkb4mhpUukeoLisnPTcYjzs6mbuj5ZOLbEytuK5kOfIKszinf3vsPzkcg6kHSDUJbROjnFHMDKlwKk1/PAgFObAiMXa+9KrOQVC+gk4txu8O11Z3u0laKYt4WVhbIGfjZ8EnYUQQghxz/nkk08ICgpi8eLF9OvXj9mzZ1c76AyQn5/PgAED+OGHH3B2dmbnzp26deHh4QQFBfH222+TkpJCZmYm8+bN4/3336eoqOp5T25E7tbuVHY+N6y3fNmYZmMYHTy65sdoOQK8wmq0i4OlCQrIyi/RLVu0O4FujZ3wvirrp2eQM5tj0jiTkcep9Ev0Dnatef9qycig7hP53W3Mrzm50GXbT2WQmVfMykNJDGntUed9EEKIf5NhjYfxUeRHPLL2EaLSo6q1T1R6FOcvna9VaYZgh2BismJQSvHegfcItA9ke+L2a24fnRUtgec65GjuyMK+C/m699c0MGlQ392pEZ+ryoxFJ+dyqaiUc1kFuNuaYWxYN7fSIc4h/DjgRyyMLXiq1VMYGRixOHoxCbkJNHX8d9UvznPvDEUXYdhCMKriaT3HxpB1+u/A8/UnvRZCCCGEuNc8++yzHDlyhMcee4xFixbxwQcfsH79er1tTE1NKSm5ErcrKCjAzOzKk6bBwdqytG5ubnq1oK8WEBDAqFGjmDx5MtOnT6eioqJW/ZVSG/8CfjZ+YHN7jqXRaGjlacuB+Av0aerKwYRsFuyMZ/E4/XT7tr72nLtQwLjvD/Bg64aYGN3d33G425qRfPHaGc9KKSYtO4yBBlxtzAhwubs+VAshxO0W7hmOqaEpF4ouMPHPiXzV6yua2De55vbzj8xnUfQiJreZXGlytupwt3SnuKKYPxL+IKMgg4+7f8zjfzyOUqrK7Nvjmcfp1LBTFS2J2nK3qtsnnW4XH0dL4rPy6YoTb6w6zsBW7liaGNLUve5uvjQajW6ujoeaPATA2GZjiUyLrNX5fifL8RuIW69nwNSq6g1MrcDKSVtqw8r59nZOCCGEEOIO98QTT+Dj48OUKVMYM2YMCxcurLRNcHAwGzZsYOhQbYWDv/76i+bNm+vWX+/pQ41Gg1KK2NhY8vPz+eqrr0hPT2fEiBF07969xv2VwLOosS4Bjmw/lUnqxSK+3nGW53sG0MRVP9BqbGjAqmc6k5BVQBsfu3rqad2xtzShsKScgpIyLEwq/9okZhei0cDD7bwIcLnGBykhhBA6BhoDOnloA7vxufGsPbv2moHnrMIsFhxbwLL7luFp7VnlNjei0WgIsg/i3f3vMjp4NH62fpgZmXHiwgmCHPRrDBeUFpCUl4Snee2OJf5dfB2uZDyfycznYPwFrM2NCfGyvaXHtTG1obtXzW/u73gag2sHnS9zCtROfi2EEEIIIfQcP36c4OBgEhMT8fSs+vPKSy+9xKuvvsqPP/6IkZERnp6eTJ8+vVrth4SE8NJLLzF37lz27dvHr7/+irGxMc8991yt+iuBZ1FjnRs5sXjvQXbFZfHR8Fa08bGvcjtPews87atXLuROp9Fo/q7zXEQj58oflg6dz6G1ly2TejWuh94JIcTdrY1LG+YennvN9X8l/kWYe1itg86XBTkEEZkWySD/QQB0adiFnck79QLPZy+eZVXcKgLsAm5J6SZx9/FxtGRXXCa5RaVk5RdzICEbOwsT7m91d2Zw3xVajgCr21emTQghhBDibjFixAjd/2fOnFnlNi4uLnz11VdVrtuyZYvu/y+88AKFhYWYm5vrJg6cNGkSkyZNArT1pG/W3V3/QNSLQNcG5BeXYWpkQKj33Z/NXF3utmak/KPcxp+x6Ty1+CCRCdmEeN07r4UQQtSllk4tOXHhBMXlVdcX+/P8n3T3vPnMz24NuzG66WhszbSZqm1d2nIg7YBufX5pPk9ufJLMwkyeaPHETR9P/Ds0drHiROol4jPzCXS1Jr+4jJNpl+q01Ib4h2ZDwEdK3QghhBBC3O0klUfUmIGBhoEt3WnR0OaumZW+LrjZmOvNaq+U4p31saRcLKS0rIJvx7Stx94JIcTdy8LYAj8bP45mHKWNaxu9dfml+exL3cfMTlV/m18ToS6hhLqE6n5u7dKa/9v5f+SW5DLv8DzO5JyhvVt73bFiYmJu+pji7udlb0FpeQU7Tmfi52SJh60ZGZeKMTM2rO+uCSGEEEIIcUeTjGdRK68PbMrgkIb13Y3b6nKpjcs2xaSjAb4Y2ZrSckXzhpL5JIQQtdXapTWR6ZF6y5RSTN89nT4+fbAxrftrrJ2ZHa6Wrryx6w1iL8QS4hzCi21frPPjiLubRqOhjbc9Px9IxM/Rki4BTnRs5Fjf3RJCCCGEEOKOJ4FnIarJ3caME6m5XMgvAWDdsRRGhXnRsZEju6ZFVDnpoBBCiOoJcwtjZ9JOvWUbEzYSlxPHy+1evmXHDXUJZVPCJqa1m8aTLZ/E2sT6lh1L3L3a+NhxJjMfHwdLHu3ow9S+gfXdJSGEEEIIIe54EngWoppCvOxIzS0m4v2tFJeVE52cS3MPbQaeo5VpPfdOCCHubmFuYZzKOUVGQYZu2caEjYwKGoW5kfktO25v796MDBpJI7tGt+wY4u7X9u+JlH2dLOu5J0IIIYQQQtw9JPAsRDU1cW3Ab093wsPWnMiEHM5m5tPYpUF9d0sIIf4VTAxN6NawG5vPbQagrKKMXcm76OzR+ZYet51bu1uaUS3+HYLdrXFuYIq/o1V9d0UIIYQQQoi7hgSehaihNt52LNt/Di97C5lYSAgh6lAv716sjltNSXkJhzMO42HlgbOFc313SwiMDQ3YM60HNhbG9d0VIYQQQgghbqnS0lKmTJnCiBEjGDlyJHFxcbVuSwLPQtRQqI89a46mEOwudUCFEKIudfHogpOFE6PWjuLDgx/e8mxnIWrCwEBT310QQgghhBDiltu2bRtlZWUsXbqUp59+mo8++qjWbclsaELUUFsfO0rLFcFuEngWQoi6ZGxozIfhH7Ll3BayirKI8Iqo7y4JIYQQQgghxD3F19eX8vJyKioqyMvLw8io9uFjCTwLUUNuNuZ42JpLxrMQQtwCGo2GHt496rsbQgghhBBCCFHven+4jZNpeXXWXmMXK/6Y1O2621hYWJCUlES/fv3Izs5m3rx5tT6eBJ6FqIVFY9vh7SAz2wshhBBCCCGEEEKIW+NGQeJbYeHChXTu3JkpU6aQkpLCo48+yurVqzE1Na1xWxJ4FqIW/JxkVnshhBBCCCGEEEII8e9ibW2NsbF2Um0bGxvKysooLy+vVVsSeBZCCCGEEEIIIYQQQgjBmDFjeOWVVxg5ciSlpaVMmjQJCwuLWrUlgWchhBBCCCGEEEIIIYQQWFpa8vHHH9dJWwZ10ooQQgghhBBCCCGEEEII8TcJPAshhBBCCCGEEEIIIYSoUxJ4FkIIIYQQQgghhBBCCFGnJPAshBBCCCGEEEIIIYQQok5J4FkIIYQQQgghhBBCCCFEnZLAsxBCCCGEEEIIIYQQQog6pVFKqfruxO0QFRWFqalpfXdDCHGHKS4uplWrVjXaR64nQoiqyPVECFFXanM9AbmmCCGqJvcoQtw9SktLCQgIqO9ucPToUT766CO++eYbLly4wIwZM8jNzaW8vJz33nsPLy+vKveLiYkhKChI9/M9E3gWQgghhBBCCCGEEEKIO9U/A7f1Yf78+axatQpzc3N++uknXn75Zbp27Ur//v3Zs2cPRUVFhIeHV7nvP/svpTaEEEIIIYQQQgghhBBC4OXlxaeffqr7OTIykrS0NMaMGcPq1atp165dtdsyuhUdFEIIIYQQQgghhBBCCHETPg+DjJi6a88pCJ7ec91N+vTpQ2Jiou7npKQkrK2tWbhwIZ999hnz589n4sSJ1TqcBJ6FEEIIIYQQQgghhBDiTnODIPHtYGtrS0REBAARERF8+OGH1d5XSm0IIYQQQgghhBBCCCGEqCQ0NJRt27YBsH//fho1alTtfSXwLIQQQgghhBBCCCGEEKKSqVOn8ttvvzFixAi2b9/O+PHjq72vRimlbmHfhBBCCCGEEEIIIYQQQtxATEwMQUFB9d2NWvtn/yXjWQghhBBCCCGEEEIIIUSdksCzEEIIIYQQQgghhBBCiDolgWchhBBCCCGEEEIIIYQQdcqovjtwu0RFRQFgampazz2pH8XFxffk2O/VccO9O/aajru4uJhWrVrV6BhyPZFz615zr45drie3npxb9557dey343oCEBkZibm5eY33+zeQc+veI2O/tdcUuZ7ce+fWvTpuuLPGXlpaSmFh4W07nlIKjUZTo+0tLCyqvf1tCzxXVFTwxhtvEBsbi4mJCbNmzcLb21u3fsuWLXz++ecYGRkxZMgQHnroIUpLS3nllVdISkqipKSECRMm0KNHDxISEnj55ZfRaDQEBATw+uuvY2Bw/eTtyyfQ3Vyg+2bc7cXJa+teHTfcu2Ov6bhjYmJqfAy5nsi5da+5V8cu15NbT86te8+9OvbbcT0B0Gg09+TrC3Ju3Ytk7Lf2miLXk3tv7PfquOHOGntMTMxt/dKnsLCwRseraVD8tpXa2LRpEyUlJSxbtowpU6bw1ltv6daVlpYyZ84cvv32WxYtWsSyZcvIyMhg1apV2NrasmTJEubPn8/MmTMBmDNnDs8//zxLlixBKcXmzZtv1zCEEEIIIYQQQgghhBBC3MBty3g+ePAgXbp0AaBVq1YcO3ZMty4uLg4vLy9sbGwACA0N5cCBA/Tt25c+ffrotjM0NATg+PHjtGvXDoCuXbuyc+dOev0/e/cdHld9Jf7/PX00Vb1btiy5F8DGgAnVtEAq64ANrDdZSNkQQrLJ5kfIsiGBLE42gS+BhGRDErKB0NIogRDA9G4DsrEtVxWr1+m9/f64tmRZZTRjzYyEzut59GDNbeczurpizj33fC64IFtDEUIIIYQQQgghhBBCiA+dWCzGTTfdRHNzMxqNhs2bN+Pz+YYKgvPy8vjRj35EcXFx0n1lLfHs9XqxWCxD32s0GqLRKFqtFq/Xi9VqHVpmNpvxer2Yzeahba+//nq+/vWvAyP7j5jNZjweT9Ljh0IhEolE2o+tzXTBYHBWjn22jhtm79izMW65nsi5NdvM1rHL9STz5NyafWbr2LM17ng8PivfX5BzazaSsWd27HI9mX1jn63jhuk19lz0eD72eC+88AKxWIz77ruPrVu38t///d94PB6+9a1vsXjxYh5//HHuvfdebrzxxqT7z1ri2WKx4PP5hr6Px+Notdoxl/l8vqFEdFdXF1/5yle48sor+cQnPgEwop+zz+fDZrMlPb70UJw+/WqyabaOG2bv2KUna+bJuTX7zNaxy/Uk8+Tcmn1m69iz1eNZrVbPyvcX5NyajWTsmb2myPVk9o19to4bptfYp0OP54997GNcdNFFaLVaBgYGKC0t5dZbb6W0tJRAIEAsFpv0ZIxZSzyvWrWKF198kUsuuYSGhgYWLlw4tKyuro7W1lacTicmk4lt27ZxzTXX0N/fz9VXX813v/td1q5dO7T+0qVLefvttzn11FN55ZVXOO2007I1DCGEEEIIIYQQQgghhMi4Sx+/lAPOA1O2v/r8ev76qb8mXU+r1XLDDTfw3HPPcdddd1FaWgpAQ0MDDzzwAH/4wx8mdbysJZ4vuOACXn/9dTZu3EgikeC2227jySefxO/3s2HDBr797W9zzTXXkEgkWL9+PWVlZfzgBz/A7XZzzz33cM899wBw7733csMNN/Bf//Vf3HHHHcyfP39EH2ghhBBCCCGEEEIIIYSY6SaTJM6UH/3oR/zHf/wHl19+OU899RQvvfQS99xzD7/61a8oLCyc1D6ylnhWq9XccsstI16rq6sb+ve6detYt27diOU33XQTN91006h91dbW8sADD2QmUCGEEEIIIYQQQgghhJiFHnvsMXp6evjSl75EXl4eKpWK5557jkceeYRf//rXlJeXT3pfWUs8CyGEEEIIIYQQQqRrf4+HBWXWXIchhBAfahdeeCE33ngjV111FdFolO985zt85zvfoaKigm984xtoNBrWrFnD9ddfn3RfkngWQgghhBBCCCHEtBYIx7jwzld47NqPcMKc/FyHI4QQH1omk4mf/vSnI147//zzgbEnI5yIekojE0IIIYQQQgghhJhig/4wiQT85Nm9uQ5FCCHEJEniWQghhBBCCCGEENOawxdmYZmFpj4fOztcuQ5HCCHEJEjiWQghhBBCCCGEENOawx+m1GrknEUlvN08mOtwhBAiYxKJRK5DSMtYcUviWQghhBBCCCGEENPaoC9MgVnPiXPy2d7mzHU4QgiREUajkYGBgRmXfE4kEgwMDGA0Gke8LpMLCiGEEEIIIYQQYlpz+MIUmHScOCefn714AE4sz3VIQggx5aqrq2lvb6evry8rx4tEIuh0uilZ32g0Ul1dPeI1STwLIYQQQgghhBBiWnP4IxSY9NSVWBj0hnEFY7kOSQghppxOp6O2tjZrx2tsbGTJkiUZW19abQghhBBCCCGEEGJac/jDFJr1qNUqVlTb2dcfynVIQgghkpDEsxBCCCGEEEIIIaa1QV+YfJPyePeKKjsHBiXxLIQQ050knoUQQgghhBBCCDGtOf0RCs16AEptRmm1IYQQM4AknoUQQgghhBBCCDGtDfrCFJiUxHOBSYcrGM9xREIIIZKRxLMQQgghhBBCCCGmNYc/TMHhiucCsx53SCqehRBiupPEsxBCCCGEEEIIIaY1hz9M4eGK50KTHo8knoUQYtqTxLMQQgghhBBCCCGmrUA4RiIBeXoNAAUmvbTaEEKIGUASz0IIIYQQQgghhJi2Bv3hoYkFAQrMOmm1IYQQM4AknoUQQgghhBBilvnlywdJJBK5DkOISXEcNbEggMWgJRpPEIxI8lkIIaYzSTwLIYQQQgghxCzz8xcP4ApEch2GEBNy+MIkEgn6vSGKLMOJZ5VKhc2gwemXc1gIIaYz7UQLvV4vf/3rX9m6dSsOh4OioiLWrl3Lxz/+ccxmc7ZiFEIIIYQQQggxhUosBvq9IfKPqiIVYjpJJBJces/r3PZPK2gd8FNTaBqx3GZQM+gLU2435ihCIYQQyYybeP7zn//MM888w9lnn82mTZsoKSnB7Xazfft2vva1r3HRRRdx2WWXZTNWIYQQQgghhBBToNhioM8Tpr4015EIMbaGNictA372dntoHfAzv8QyYrnNqMHpD+coOiGEEJMxbuK5uLiYe++9d9TrK1euZNOmTbz88ssZDUwIIYQQQgghRGYUW/X0e0Ps7nQTTyRYXmXPdUhCjPB4QycVdiP7erx0OAOcvbBkxHKbQcOgJJ6FEGJaGzfxfPbZZ0+4YbLlQgghhBBCCCGmp+LDrTbeO+RAq1ZJ4llMK/F4gqc+6OLfz1/IX99vp8sVpLZ4ZLtPm0GNwyeJZyGEmM4m7PEM8Mtf/pJf//rXGI3DfZNee+21jAYlhBBCCCGEECJzjiSe2wYDFFukz7OYXvq9IeLxBBcsLWPz042EYnGqC/JGrGMzaBj0yeSCQggxnSVNPP/973/n1VdfJS8vL9mqQgghhBBCCCFmgGKLge1tTtodfvRaVa7DEWKELleQinwjxRY9Go2KalseWo16xDo2owaHtNoQQohpTZ1shaqqqhHVzkIIIYQQQgghZrZii9Ljud0RwBOM5jocIUbocgWosOehUqlYWGpl/jFtNuBwqw1JPAshxLSWtOI5EonwiU98goULFwKgUqm4/fbbMx6YEEIIIYQQQojMKLYaONDnxRuK4g5IuwIxvXQ6g1TalQK4+jILZr1m1Do2g4bBHkk8CyHEdJY08fyFL3whG3EIIYQQQgghhMiSEouB1gE/eTqNVDyLaafbHaTcrrT7/PLZdWjUo9vB2IxqnH65aSKEENNZ0lYbCxcupLe3l87OTjo6Onj//fezEZcQQgghhBBCiAwpthgAWFxhxR2U5J2YXjqdASrzlYrnOYUmKvNHzzmlTC4oFc9CCDGdJa14vv7665k3bx779u3DYDDIJINCCCGEEEIIMcPl6TWY9RqWVdrY1eHOdThCAPD6gX50GrUyuaB94tyDzSCTCwohxHSXtOIZ4JZbbqG2tpb77rsPl8uV6ZiEEEIIIYQQQmRYsdVAfYkFgGAkluNohIBHt7Xx61eb6LZu/7cAACAASURBVHYFqTjc43k8Jp2KSCyekXP3e0/sYmeH5D6EEOJ4Ja14BgiFQgQCAVQqFX6/P9MxCSGEEEIIIYTIsFKrgZoiE7Y8Le5gBKNu9ARuQmTTvh4vLf0+YvEEZbaJE88qlYp8kx6nP0K5ferO3Xg8wZ/fa6fQrGd5lX3K9iuEELNR0ornq666it/97nd85CMf4eyzz2b+/PlpHSgej/Pd736XDRs2sGnTJlpbW0csf+GFF1i/fj0bNmzg0UcfHbFs+/btbNq0aej7Xbt2ceaZZ7Jp0yY2bdrE008/nVZMQgghhBBCCDFb/b8NJ3LWghJsRp1MMChyLhZP0NTnpbbYjN2kQ69N/oB2oUk/5X2em/q9eIJR3mkenNL9CiHEbJS04vmiiy4CwOVycfHFF2OxWNI60PPPP084HOaRRx6hoaGBH/7wh/ziF78AIBKJsHnzZv70pz+Rl5fHFVdcwbnnnktJSQn33nsvTzzxxIje0rt37+Zf//Vfufrqq9OKRQghhBBCCCFmu+oCEwBWoxZ3YHiCwbZBP7F4gnnF5lyFJmah1gEfxRYDnzihkmd2dk1qmwKzbsr7PL/X6uTcRSW80zxIJBZHp5lUh1IhhBBjSHoF3bp1Kx//+MfZuHEjv/nNb/jjH/+Y1oHeffddzjzzTABOPPFEdu7cObTs4MGD1NTUYLfb0ev1rF69mm3btgFQU1PD3XffPWJfO3fu5KWXXuKqq67iO9/5Dl6vN62YhBBCCCGEEGK2s+XpcB9V8fzgO4e499WmHEYkZqP9vV4WlllYv7qKL59TN6ltCs36qU88H3JwzqJSaorM0udZCCGOU9KK5zvvvJMHHniAr371q/zbv/0bV1xxBZdddlnKB/J6vSOqpTUaDdFoFK1Wi9frxWq1Di0zm81DyeSLLrqI9vb2EftauXIll112GcuXL+cXv/gFP//5z7nhhhsmPH4oFCKRSNDY2Jhy7B8GwWBwVo59to4bZu/YszFuuZ7IuTXbzNaxT+fryc6eIFa9mrkF+gxFlh1ybs0+s3Xs2Rp3PB5P6ziJcIA9B1sojfUDcKC9l15vdEb9rOTcmvle3+mgSBtnoL2ZuRpobHROuH4wGISQj8aDh6jTTV2C+M393ZxeFmeBHZ54aw9GX/6U7XuqZOPnnu715MPgw/R7lYrZOm6QsWdy7EkTz2q1mvz8fFQqFQaDAbM5vcetLBYLPp9v6Pt4PI5Wqx1zmc/nG5GIPtYFF1yAzWYb+vett96a9PgGgwGAJUuWpBX/TNfY2Dgrxz5bxw2zd+ypjjudC6xcT+Tcmm1m69in8/XkrnffZVG5mY+evjDpuuFonH5viMr8vKTrZpucW7PPbB17Nq4noHx2S+f9rd4dwVqYz5IlNQBE3/LQ5XPPqJ+VnFszn7Phfc5aWsKSJdWTWr+xsZH5VSZ0GjVLlixI+7jdriAlVgMatQp3MEK/v5WL166kqHyQ255u5ObLp9/7m41rSrrXkw+DD9PvVSpm67hBxp7J60nSVhs1NTXcfvvtOJ1OfvWrX1FZWZnSAY5YtWoVr7zyCgANDQ0sXDj8Qamuro7W1lacTifhcJht27Zx0kknjbuva665hh07dgDw5ptvsmzZsrRiEkIIIYSYifb2eOhxhya17t92dHLDn3dkOCIhxExmy9PhCQ73eO73hun1hHAf9dpYnt/dw6Pb2jIdnpgFXIEIbxwc4MQ5qVUXF0xBq41r//Au/9jVDUDDISfLquzoNGpOm1/EoC/Mnm73ce1fCCFms6SJ5+9///tUVlayevVqTCYTP/jBD9I60AUXXIBer2fjxo1s3ryZG2+8kSeffJJHHnkEnU7Ht7/9ba655ho2btzI+vXrKSsrG3df3/ve97jtttvYtGkT7733Htdee21aMQkhhBBCzDTBSIyWfh897uCk1t/R7qLPM7kktRBidrIatCOSzP3eEAUmHU19vgm2gq0tg9z53D6isXimQxRTLBiJcdeW/bkOY8jtz+7lwmVl1Jdakq98lALT8U0umEgkONDr5Z3mQUDp77yqpgAAjVrFpSdV8ed32yfahRBCiAkkbbURCAQoLS3FbrcD8Nxzz3HJJZekfCC1Ws0tt9wy4rW6uuEJA9atW8e6devG3La6uppHH3106Ptly5bx8MMPpxyDEEIIIcRMd6DXi1qlots1ucTzrk4X/V5JPAshxmfL09Hbq1wnEokEA94w5y4u4WCvd8IK1EFfmE5XkBf29HLhsvJshSumwOsH+vnFSwe5/rz0W1RMlW5XkMcbOnn5W+ekvG2BWc+gL/3Ec783jDsYZWvLkcSzk38+tWZo+brFpdz61Ozs+yqEEFMhacXz1VdfzfPPP09DQwMNDQ1s3749G3EJIYQQQogx7OvxsHpuAb2e5InneDzB7k43Dn9EKhKFEOOyGrVDrTbcgSgGrZqlFXYO9nkn3G7QF+ZjKyp44O1D2Qhz2mod8PEvv30n12Gk5PnGXgKRGLF4Iteh8MzOLs5bUkq+KfUJcwtNepz+iVvCTKSpz8uKKjvN/T7cwQgNhxysmlswtNxq1OEPRdPevxBCzHZJK56tViubN2/ORixCCCGEECKJvd0ePlJfzHuHHISjcfTa8esImgd8FFr0BMIxBn1hSm3GLEYqhJgpbEYd7qCSXOvzhii2GqgrNfO37V0TbjfoD/PNCxbx5T+8S783RLHFkI1wp50/v9fBmwf7iccTqNWqXIeTVCKR4IU9PahUEIjEsBiSpgUy6ukPuvniWfPT2rbAdHwVz039PhaVW8nTa7jzuf3km/QjzmOTXoM/HEt7/0IIMdslrXg+44wzeOihh9i6devQlxBCCCGEyL5EIsHOThdLKmwUWwz0JWmhsbPDxfJK+6TWFULMXvkm3VDyTkkg61lZlc+2VseoithEIsF9rzcTjycY9IWpzDdy7qJS/r6zOxeh51wikeCx9zuIJ5h2bY22tQxyoNcz6vUd7S7Mei1FZgP+cG6reXvdQfZ0uzlzYXFa2xeYj6/Hc1Ofl/klZs6oL+aFPT3c+unlI5abDdqcv0dCCDGTJb21uW3bNsLh8FDCWaVSsWbNmowHJoQQQgghhsXjCdb/8g1CkThr5hVQajPS7QpSlZ837jZvNQ2yotqONxSVCQaFEOOaV2ymud9HIpGgz6NULtcUmSi26HnvkIM18wqH1n3j4ADff3I3l6yoYNAXpshs4OMrK7j31SbWLS6lwmacEVW/U+W9Qw50GhXLKm10OAPT6smSB985hDsQ5defPXnotWgszvee3MWmtXP53Rst+EMxsOYuxq0tDk6pLcKg1aS1vcWgJRyNE4zEMOpS30dTn4/L5hZy4dIyrju3ftS5a9Jr8EnFsxBCpC1pxbPf7+fXv/41d9xxB3fccQe33357NuISQgghhBBHUatVbP6nFfztq2eQb9JTbjPQ61b6PL9xsJ9gZOQH4y5XgKc/6OIzq6spsRjo84R4t3UQn/SqFEIco8isR6VS2mwc3TLjo8vLeeaYSubfvdECQEu/j0A4htWo5exFJQQiMc67/SVe2teb7fBz6pV9/VywtJyq/Dw6nIFx12sb9A9NYJctTn+ELXt6aOn3Db320y37Mek1fHbtPEx6Lb4cVfP2eoIkEglaBnzUlZjT3o9KpSLfpMMdSK/Pc1O/cny1WjXmDRODVk00Fpd5EoQQIk1JE88LFizgqaeeoqmpiebmZpqbm7MRlxBCCCGEOMbictvQB+Mym5Huw4nn//zrTh5v6ADAHYywdvMW/vW+rWxYM4dSq5ESq9Jq40v3v8fXHm4gPg0mkxJCTB8qlYoFpRYO9HrHTDwnEso1o8MZYFvLIOcsKmFnp5t8kx61WoVBq+FvXz2TjWtqaOrzTXSoD52GNieravKpzM+jc4LE821PNw4l7bPF6Q+zZl4h97/VCsCj29p4rKGD/7fhRNRqFeYc9i++6t63efPgAC39PuYVp594BrDn6XCmkXh2+sP0uIPUFJnGXUelUmHWa/FHpOpZCCHSkbTVxp49e9izZ8/Q9yqVit///vcZDUoIIYQQQkzs6MTzgDfEo9va2bCmhv09XiwGLZ86sYorTpkDQLHFwNaWQVQqcAci/O6NFq4+ozaX4Qshppn6UquSePaEWTnHDsCiMis6jYpdnW6WV9l5eW8f5y4qpciiZ2eHi0KzbsQ+agpNtA7MnsRzIpGgoc3Jjy9bSZsjQNugf8RyTzCC1ahjX4+HZ3Z1c+aCkqzG5/RH+OJZ83lkWxsAdz63j1/9y8mUWpV2ICaDNidPwXhDUQ70ednZ6aJlwMc/rao+rv0VmPQ4/aknnp/Y3sm6xaVJ23zk6TX4QzFsRt2E6wkhhBgtaeL5/vvvz0YcQgghhBAiBeU2Iwd6vYSjcfzhGIcG/Rzo9XKw18uKKjtfPqduaN0Sq4FX9vdx9sISrj2nnq88+B6fPX0emlnUh1UIMbH6wxXPvZ4gxZZSQCk6uuhw1fPyKjtvNg1w5oJi/KEoL+zppcRqGLGPmkITr+7vy0X4OdHc78Ni0FJqNVKVn8dbTQNDy1oHfHzyZ6/z+rfX8bMXDnDe4lIGfOlPgpcOhz/M6rkFbP77HryhKA5/hKUVtqHluap43tXhIpGA3Z1uWgb8zCsev+J4MvJNOpxpTDD4x23tfOuiRUnXMxty15JECCFmuqStNoQQQgghxPRTdnhyQYc/TIFZz6dOqOSpHV0c7PNSV2oZsW6J1UAwEuekmgJOmJNPscXAC3tmVx9WIcTEFpRaeG1/Pw1tTlbPLRh6/aPLynlml9Ju482DA6ydX0Rlfh5N/T6KzMcknotMHDqm6vfD7P1DTk6syQeg6phWG019PlyBCD/6+x5eO9DPtefW4wlmL3kZjydwB6PUFptJJBJsbRmkttg8oo+xSZ+biucPOlysnlvA1hYHnmCEMuvxTchoz9On1GrDHYxw15b99HtDfKS+OOn6psMVz8djV6eLwFFJ/jcO9g+1sBFCiA8zSTwLIYQQQsxA5XYDPZ4g/d4QRWY9H6kv5q2mAQ70eqkrGZl4PtKvdVWNkky68tQa/vJee9ZjFkJMX/WlFpr6fVxzRu3QNQPghOp8PMEIj2xtw6hTM6fQRFVBHokEFBzTamNOgYl2R2DW9JHf3u7kpDlK4rky3zhicsFDg35WVtu5/61WPrt2HhV2I55gehPgpcMdjGDWa9Bq1MwvsfDsrh7qj7kpacpRxfMHHS7+aVUVPe4gcwvNY07ql4p8kw5XCq02fvd6C68f6Of/rj5lUk/+KO9T+gn6Nw70c+nP3+CZXV0ARGJx/vnXb6fVHkQIIWaapK02tm7dOnIDrZaKigrKy8szFpQQQgghhJhYqc1IjyvIoC9MkUXPyfMK+MqDTgpM+lHJhVKrAb1GzYoqpW9rXYmFh945lIuwhRDTVIXdyJfOms81Z8wf8bpareL7n1zGt/64g48uVz4DVucrrREKj6l4ztNrsOfp6PEEqbDnZSfwHGru93HuYqUtSaFZTyAcwxeKYjZoaR3w8/GVFXxiZSWXr5mDRq3KasWzwx8h36QHYH6Jmed29/Ava+eOWMdk0OSkhcQH7S6+fE4d9aUW5k4wsd9k5efpcAYm32rD4Q9zwdIyFpZZJ7W+Sa9NO0Efjyf4yoPvcVpdEQd7lf7nXc4g8YTS67rArE9rv0IIMVMkTTzfeeed9Pf3s2zZMnbv3o1OpyMcDnPZZZfx+c9/PhsxCiGEEEKIY1gNWhJA64CfQrMBq1FHfamF3Z3uUR/kC8x6tnzzbPL0ygRKBSYdjiz3GhVCTG8qlYobL1ky5rKPLq9gVU0BKpVSHWrL02IxaCk0jZ5srabQxKEB/6xIPHc6A1TlK+NUqVRUFeTR4QywsMzKoUE/p9QW8NHlFYAyEWEoGicai6PVZP7BY4c/TMHhn09diYW/vNcx6mkYs1573C0kUuUPR+l0BagvsbC00kaJxZB8oyTyTTr2dAcnvf6RmwOTZT6OBH2HM4BBq+Ezq6v5+wdKxXO7Q2lH481BmxMhhMi2pH/xjEYjTzzxBHfccQdPPPEElZWVPPnkkzz77LPZiE8IIYQQQoxBpVJRbjOyu8tN0eGKqVNrC5lbZEI3RlJjTuFwMrrQrMchj/gKIVJQajMOTSaoUqmoys+jcIykYU3h7OjznEgk6HAGqLAP9yeeU2Ci7fDYDw36qCk0Dy1TqVSY9ZqsJRud/vBwxXOxEsdYrTayXfHc4QhQmZ+HVqPm2nPquPLUmuPeZ74ptR7PvlAMSwqJ5zxd+hXPB3q91JdaqC+xcLDPC0C7Q2nJIolnIcRskDTx7HA4MBiU/6HQ6/U4HA70ej3xeDzjwQkhhBBCiPGV2gzs7hxOPF+wtJxzF5Um3c5m1OENRYnG5P/nhBDpuWRFBUsrRrcqmDNLEs8OfwS9Ro3VOFz1Pacwj7ZBP4lEgrbBADXHPH1iNeqy1m7D4YsMVTzPL7GgVsG84pHxmA3Zr3hudwxXideXWplbZE6yRXKp9nj2hKIpJZ7NBg3+NJPERxLPtcVmWgf8RGNxqXgWqQk4ofXNXEchRNqSXm3PO+88rrjiClauXMkHH3zAunXrePDBB1mwYEE24hNCCCGEEOMotxl5Zlc3l51cDcAptYWcUluYdDu1WoU9T4crEKFoCh5zFkLMPl87f+zPg5X5Rra1OLIcTfZ1OgNUFYxM5M4pMNHmCNDnCWHSa0YlN61GLe4sTTDoDAz3eK4rMfPjz5yAQasZsY5Jr8EfyXLi2RmgumBq27Dk5+lT6vHsC0WxGCefeDbptfiOo+J5RbWdPL2GEquBdkeAtiMVz1ns+S1mkEQC2t6G6lNArYb3H4AXboUvvgyli3MdnRApS3q1/cpXvsJ5551HU1MT69evZ+HChQwODnLFFVdkIz4hhBBCCDGOMpuRYCROkTn15HG+SYfDH5bEsxBiSpXajHS7J99vd6bqcAaoyjeOeK2m0MS2VgeHBv2jqp1BedokWxXPTn+YgsOJZ61GzfrV1aPWUXo8Zzf52e7wU11w/BMKHi3fpMPhS6XVRhSzPpXEs4ZAuonnPi+XrqoClF7bB/u8tDv81BSa8EnFsxiLoxl+exHUnQeX/x80vQS1Z8NjX4Yvvpjr6IRIWdJWG11dXbz66qs0NTXx7LPP8rOf/YzCwsKhiSWEEEIIIURulNmUpEeRRZ/ytoUm6fMshJh65TYjve5QrsPIuCO9io82p1Dp8dzU52Nu4ejkqtWozV6rDX+YAvPoyR+PZjqOSfPS1XFUq42pYjcpT/BMlieYWquNdHthJxKJoVYboCSeD/R6aXcEWFxulVYbYmxdO6D+AtAa4M174NBbcOkvoWcXRAK5jk6IlCVNPH/ta1/D6/VSXFw89CWEEEIIIXKv/PCkVoXm1BPP+SY9g77JP5qcqtYBHz9/8UDG9i+EmJ7KbEZ6PFNX8ezyR+h0Tr9kS6dzdAJ1ToGJdkeAl/f1cXrd6M/NSuI5Ozf8HP7hVhvjMevTnzQvXR0ZaLVhNWgJRmJEJjlvgS+cWquNdHth93vDqFQMzcNw1sJiHnrnEP3eEPWllqzdhBAzTPcOqFoN5/4nvPoTKF4ApkKwVYKrPdfRCZGypIlns9nMv//7v7Nx48ahLyGEEEIIkXtlNqVNRnEarTYKzTqc/swlnvd0e/jLe/IBSYjZpsCkwx+OEZyi3sG/eb2ZS+95nZ7jbN/x2v5+4vHElMQE0OkaXfFsN+lQqeCFPb2cs7hk1DbZnFzQ6Q+TnzdxxbPZoMl6u4d2R4CqKU48q1TD8xYkk0gklFYbBk3SdY9Itxd264CPeUXmoafFz1lUyrJKO6VWI/kmnbTaEGPr2g4VK6F8Ocw/F+rPU163V4OrLbexzSbOQ0qVuThuSRPPCxYs4KmnnqKpqYnm5maam5uzEZcQQgghhEiizGZEq1Zhy5t85dYRBSY9gyn0xEyVKxCh3REgkZi6RI8QYvpTqVSUWg1ptdt4Ynsn97w08kmJfd0e5hSY+OL97066ovVYiUSCa/5vKzs7XWltP5YOZ3BU4hmUPs8Ly62UWo2jlmW14tkXGerxPB5Tliueg5EYLn9kzPfmeNlNOpyTaB8Viirn0LETLU7ElGYv7D5PiFLryBvD3//UMr5zyRIsBp202hBj69oBFSco/77sPjjrW8q/7XOk4jmbXv8pvPKTXEfxoZA08dzY2MjDDz/MzTffzHe/+11uvvnmbMQlhBBCCCGSqLTn8ePLVqY190aBWZ/Rimd3IEIoGqfP8+Hv9SqEGCnddhvb25z8+d2RiZV9PR7++9IVFJh03L1lf1rxOP3K9eid5sGk6/74H3uSVmvH4gla+n3MHWMCwZpCE+cvLh1zu2xWPPd5Q5RYJ34axqzX4gtF+fmLB3i8oSPjMXU6A5TbjWjUUz9fVMEk20f5Qqn1dwYwp9njud8boviYn0GxxcDHVlZgNmjwSOJZHMvTDfEo2JQJKdGblV7PAPmSeM6aRAL2/QN6G3MdyYdC0ivu/fffn404hBBCCCFEitRqFZeeVJ3WtgUmHQd7vVMc0TD34eRKm8NPqW3qq9uEENNXmc1Atyv1xHOnM8DBPh9tg37mFJoIRmJ0OAPUFpv5n8+sZN1PXuaaM+ZjN03cQmLUfl1Kj+htLQ4+f+b46/V5Qvz8xYN86sQqFpZZx12voc1Jhd1IsWV0Yvemjy/FPk6LC6tRy6FBf0qxpyMSi+P0h5MmnvP0GvzhGH/b0cWSCiufOrEqo3G1DvqnvL/zEfUlFvb2eDiltnDC9XyhGOYUE88mg5ZAGpXhfd7wmOcIKOeCtNoQo3TvhPIVMFZBgb1amWhQZF5vo5J8djRDNAzaw0+PRAIQC4PRntv4ZphxK56vv/56AM4444xRX0IIIYQQYmYrMOlxTOKx5HS5D/fabBucfpOCCSEyq8xmTKsnc4czwPwSMy/t7QXgQK+XeUVm9Fo1pVYjFXYjXe7UryndriCLyqxsax2csP3P1halIjrZkxov7+3l7EWjezgDVOXnjVtRm61WG72eEEVmQ9LKYr1WjUoFe7vdbG9zZjyu1/b3c9r8oozse0W1nQ/ak4/BE4qkXPFs0mvwpZF47p+g6txi0OGVyQXFsTxdw9XOx5Iez5nh6Yb9z6PzdQ6/tu8ZWHwJ5NdA/77h19/+X3j6W9mPcSo9fh08dq2SRM+ScRPPd911FwCvvfbaqC8hhBBCCDGzFZj1ODLZaiMYocRqoC0L1X0ii1wdEHTnOgoxzZXZjPSm0Wan0xngqlPn8uLePgD2dntYVD5ceawktFPfb5cryKq5+Ri0Gpr6feOud6QVx3iJ505ngJf39fHi3j7OWTh2O42J2LLUaqPbpbS0mAyTXsuaeYV0uYK4M5gUTyQSbGnsYd04bUiO18pqOzvak/fw9oViaSWe0+3xXGIZu8+22aCRHs9iNP8AmMe5OWOfA05JPE+5x74MT32Dku33DL/W9BLUnQelS0e22+h8H5pfVaqhZ6LeRiWpHvLA0/+RtcOOe8X9xje+MW6/wNtvvz1jAQkhhBBCiMwrMOlxTKIfZrrcgSjLKm20OSTx/KHyjxthzmmw9tpcRyKmsTKbgd2dqd2gCEZiuINRLl5ezi9fPggo/Z2PTjyX2gz0plFJ3e0KUm7LY9XcAhoOOakrsYy53tvNg6yeW0DvOP2pf/biAR57vwONSsXquQUpx5GtiucuV5CKSSaezXoNp9cVE08k+KDdxUfqizMSU1O/j2AkzrJKW0b2v6jcSsuAj0A4Rp5+/IkDfaFoyq02zHot/iR9v8fS7w2N32pDJhcUY/H3g2mc30FbFbg7IB4HddLp2sRk9eyC9b/B9Oi/DieUuxqgajV0bYfW12DXX+HT90D3DuVn5GiGwvm5jTsdL22GtdfB0k/CfR9TxpvGPDGpGveKu3HjxowfXAghhBBC5EaBSZfZiudAhDW1BbzXmvnHt0UW9ewa/0OxEIeV2Yx0p5ggPpIsrbAbCYRjuPwRGrs9bDpt7tA6pdaJK6n3dLv5r8d28sd/O33Uvk+tLSRBguYxKp7bBv38+b12Dg34+PI5dUMVzy5/hFv+tpuvnbeAUpuBpz/o4pmvnUUkHkevTT3xk63JBbtdQcom2VvfatRx6vxCPMEIDW3OjCWeX9zTy7mLS9OaDHcyDFoN9aUWdne5J7wp4AlFsRhTSzzn6TX4QlFeP9DPqpqCCRPbR5so8WwxaiXxLEbzDUDJ4rGX6U1gsIKvD6xl2Y3rw8o/CGE/zDtDScI6WyERB70VLCVQthReug20ebD7MaUtx6JLoOX1mZd4fudepeL5U/cok1aSAEcLFNZm/NDjXnGbm5vH3eiUU07JSDBCCCGEECI77Hk63MEosXgiaR/QdLiDEZZX2nm8oTP5ymJmiARgsEnpMynEBKry8+h0ptY/ssMRoNKeh0qloq7EzIE+D7s6XCyvGq6QLbMZRiWO9/V4eLyhgxPsYfZ29tDQ5iQai6PVDCeGu90BKvKNGHRqnt3VM/T6c7uV1g9PbO/kzYMD/L8NJ+INRTlweOLVA30etuzp4cW9vZy1oJhllTZqikzpvCWAct11BTJf8dydQsXzff+6hgq70pP7mZ3dSddv6vPydvMgV5xSk1JMB/t8LM1QtfMRK6ry+aDdOWHi2ReKYtGnlng2aNUYtBo+/3/b+MllJ/CxlRVJt0kkEvR7xp/g0WxQktmJRCJjyXgxA01U8QxKuw1X+/RIPL/9vzDnFKg8KdeRpK9vL5QsApWKQMkJ6FrfVCYSrDxRWV6zFk79MhTVwWt3QukSqD0LWl+HVZuUdXb9VfmZnP7V3I0jGechePE2+MIWMBx+4qdmLRx6MyuJ53Fv0/b19Y37JYQQQgghZjatRo3FoB2aBHCquQMRFlfYGPCGM9o3VGRR3x6lCmigKdeRRfZ2UQAAIABJREFUiGmuMj+PXneISCw+4vVjvz9apzNAZX4eAHWlFl7d349KBeVHVe4ePWlhLJ7gR8/s4cp736ahzckjO5y8uLeXaDxBm2Nk0rvLqSRi5xdbhno8P7m9ky/8fhsH+7y0Dfr55ImVXLisnBKrYaiquscd4tTaQh790lq6XMER1dfpyDfpcPojE05wOBW63cFJ93iuzFeS/fOKzJNqjbS93clTO7pSjmnAO36/46mysMzCgT7vhOuk02pDpVLx7n+dz7+snUvLwPg9wkcc5/BkhOMdy6DVoEJFKDr+74SYhXz9YJpgAs7pMsFg2AdbboU/XAY9u3MdTfr6GqFUqTD3F5+gJGI7G6DicOLZXAwX/xAWXay01yhfAXNPh7a3h/ex9Tew56kcBJ+Cgy9A/fkjq7Tnnq4k0KOZe/rxiHETz9ddd93Q1/LlyzEYDCxevJjrrrsu40EJIYQQQojMKzTrGcxQuw13MEqRRc/pdUVsaexJvoGY/np2Q/068PZAJPU+u2L20GnUlNoMdByVAN7V6eKyX745at0jyegOZ4CqfCVZWldi4bH3O1heZR9RDVpmMwxNLvjotjZeP9DPM18/k59fuYq32/3s7/FyWm0RB3uHk4+JRIIuV5Byex61JWZa+n04fGG+/+Qu5hebaerz0TrgZ26hGVDaefQNJZ6VlhX1pRYe+dJaPro8eaXrRIw6DTqNKuMtFpSe1pNLPB9RVZBHuyN5lbrDF0kr/gFfmKJx2k5MlXlFZloHJk6ee4Kpt9oAJVE8r1g5fyaj3xOi2Dpxol3abYhR/P3jTy4IwxXPuXbgeaheDWu+AO/+LtfRpK93D5QsAcBXtgb2/A32Pj26itteDWUrlMRzUb0y0XIkCJ4eJVHdvVPpvT1dNb0E888Z+dq8M2D7w3DHElTR1J5QSlXSxlS33347f/nLX9DpdDz22GP86Ec/ymhAQgghhBAiO5Tqu6lPPEdjcfxh5XHmj62s4KkdyR/fFjNA724oXwn5c5S+gEJMYG6RiUODw0nA5n4f+3o8I6p93211cNptWzjY5+XQoH+o4rm+1ELLgJ/llfYR+zySFA5GYty9ZT/f++Qyii0G8k16zphr5rS6IpZU2Dh4VNWrOxBFq1ZhMWixGLRYjVp+90YLp9QWcv7SMpr6lWPXFCotNEqsBvq8wxXPk+2VPFkFZj0OX2afAlH6ZeeltE2RWU8wEkuaCHUGIvjSSJb2e0MUmTNb8VxTZEqaePaFolgMk+vRfKx5ReZJVzxP1N/5iCPtNoQY4htI0mpjmlQ8Nz4JSz4JFSfAwIFcR5O+oyqew/ZauPR/IRoau33Ipb+E5Z8BjQ4K5inj3v04LL4E8vKViujpKB6Hppdh/tkjXy9dAte+Dd/cS0Kb2t+LVCVNPG/dupW77rqLz33uc9x9991s27YtrQPF43G++93vsmHDBjZt2kRra+uI5S+88ALr169nw4YNPProoyOWbd++nU2bNg1939rayhVXXMGVV17JzTffTHw631kQQgghhJimCk16BjOQAPGGolgMWtRqFecvLeOtpgFpt/Fh0LMLypZBYd3M/qApsqKmcGTiucsZxB+O0ecNEYsryedX9/dRYjXwybtf453mQU6pLQSUxDPA8qpjEs82A32eEH/c1sbiChuraoZ7+X5hTRH/fely6krNIxLP3e4gZUe1nagtNvO7N1q4eHkFtcVm9nV76POEqDhcbZ2fp8MfihGKxuh1Bykdp0dvuoqm4EmTg31e+r1KAv7v+9wjlsXjCfo8IUptqcWtUqmoLjCNqFIfi9MfTitZOuANUzzF7+Wxqgvy6HYFJ2zp4gtHsRh0ae1/XrGJ5v7k7UhgcolniyE7k02KGSISgHhEmUBwPPbq3Fc8R4Kw/1lY/HEoXgAD+3MbT7oSCWWyvcMVzwAsuAD+fefYVefly5UEMyh9ofv2KBXSSz+l3JTv2p6duFPVvUNp3zLW/BzF9aBJ/QmQVCVNPEej0aHE7vE0vn/++ecJh8M88sgjfPOb3+SHP/zh0LJIJMLmzZv57W9/y/33388jjzwy1Ev63nvv5aabbiIUGp69ePPmzXz961/nwQcfJJFIsGXLlrRiEkIIIYSYzfJNehwZqHh2BSLY8pQP9jajjtVzC3htf/+Ide58fh89XklGT3thvzKLeyIB3R9A2XJlkp3Bg7mOTExzcwpNtB2VeO50KQnN1gE///bAuzze0MFbTQPccPFiHr/uI7z0rXOYX6IknOcWmjBo1ayoHpl4Nmg1mA0afvlyE1d/ZOSESCadmlKrkboSC019w1WpTn+YQtNwpe38EjOBSIxzF5cyv9jMawcGKLMb0B2ejFCtVlFk0dPvDdPrCVGakYrn47vu/uBvu/nh3/fw5PZO7nqzf6g1CEC/L4TFqMWoS72qtyo/jw7nxIlVpz/1VhvBSIxwLI41xd7KqTJoNZRYDRMmzz3BKOY0K57LrEZ8oSiewzdSA+EYX/z9NnZ3uket2+cJjTux4BFWg1YqnsUw/+Fq54lybvlzclPx3PQyBA+f5/v+rvRAtpZB/lyl3UQks60aMmLgAGiNYKtMfduSxUqiuX0bzDsTKlYqCd7pqKsB5pya0xCSJp4vueQSrrjiCm677TauvPJKLrnkkrQO9O6773LmmWcCcOKJJ7Jz586hZQcPHqSmpga73Y5er2f16tVDldU1NTXcfffdI/a1a9cuTjnlFADOOuss3njjjbRiEkIIIYSYzQrNuuNOgIzFHYhizxuuKPtIfRFvHhxga8sgX/z9Npz+MD9/8QDNg5mf0EQcpw8ehb9+CQYOKh/Q7FVK4vnA8+AfzHV0Yhobq+LZpNfQ3OfjrYMD/OKlg+xod7FmXiH1pdahxC8ok59u+ebZVOWPfvy31GpEpYLT68bugzq/ZGTFszMQwW4avh7VlVg4e2EJFoOW2hIz/d7QUJuNI0qsSmW10uN5aqt0C016Bo7juhuPJ3i31cE/dnbzm9easRvVvHFw+MZe26CfOceMZ7KqJ9Hn2eEP4wvHUpogsd8botisT7uILRXzik20Do6fPPcdfiInHWq1irmH23nE4gm++tD7bNnTy/5ez6h1d3a4qT98I2U8VqMWZ4Ym+BUzkC9Jf2fITY/n9m1w/6Xw1j3K99sfgRM2Kv/WaKFgLgzOwEmHm16C2rMmTvSPp2SR0h+5bCkYbYcrnqdp4tnTnV5yfQolTTyfc8453HrrraxatYpbbrmFz33uc2kdyOv1YrEMX3g1Gg3RaHRomdU6/DiB2WzG61X+Z+Giiy5Cqx35h+Hoymuz2YzHM/pCL4QQQgghJqZUPE/9h153MILNOJzoOb2umDebBnj4nTae3d3DbU83EoklcIViU35sMcUcrdD6JjS9CDWHK2ZWXK7MjP6bC3Ibm5jWjk08d7oCrJlXyIt7e7Hl6QhF4ywss46bBKwuGDt5WmY3cvnJc1Crx04WlFgMJFAmKwRw+SPkH3Uj7KpT5/Ljz6wcWtdi0FJzeGLBI0qtBnrcQSXxbJ1eFc/7e70UWQx8dHk5Tn+EjStGPlGiTJSYXuK5qiAvaasNVyBCLJ4gFJ18u8sBb+YnFjxibpGZ1nH6MAcjMRq7POOeW5Pbv4mWAR93Pr8PdzDC5SdX4zzm72g8nuCFvb2ct6R0wn3Vl1rY3yO5DHGYv19piTARUzGEvMrTSNkQj8NfvgDn3ghbf6PchG59A5Z8YnidogXQPwPbbTS/ArVnJ19vLCWLwNc7PGFf+QqlHdl05OlWqtNzKOmtvv/8z//koYceYuHChcd1IIvFgs83/AcgHo8PJZSPXebz+UYkoo+lVqtHrGuz2ZIePxQKkUgkaGxsTCf8GS8YDM7Ksc/WccPsHXs2xi3XEzm3ZpvZOvbZcD0Jud0094dobJx85dpk7G71oooGhscVT9Dt9NPh8HHZcjuPbmun0qpl0BuSc2uaqzy0E1ssTOTl2xlcdCWOI3HXfZFFDQ+xf8c24jrzxDs5ykwa+1TK1rjj8fi0eX+DoRjNfR527tqNRq2ird/Lycvs3P9+P2trTKytMeMPpx7vPy81UGwKj9ru6Pf49Gojv/rH+2xcWcDeFiexQGzU+l2H/1th0WCMekcsL9GFeWrrfgLhGF2tB+iewkrdmN/JfmeCxsZQ8pXH8NReN/V2FZ+oVbOqqACrJsb3X+li924dKpWK9/Y6MMXT+7ui8nvZ3epjok17ncrn9vd3NpJvnFzLioZ2PwZG/8yO11i/V3lRL399p4/7X9vPTy6uRH9UJf3jjS7m2tTEHe00OtI7plUV5ObHdqBRqbjzY5U8ucfN/lYvjQXDCft9/SGM6jj+3kM09o6/r/yEl9f2+jivIvV2G9m4puTieqKKhbF0vIynJrc3NnPxt8rWsgNLVE9nkuPW5ZXQ9v4rhG1zpzyGY8et83YwN+jjQMnHqba9iPmetfQt+zyDTcNV16WqfGJ73mRAvWjK4xkhkWDOy9fTfsb/HP9keIk4Cw6+RPOCLxA9PN5UfuaqaJRFKjWHNPPwNzZCIsHCoJsD298mrk+en8ym6q79OPOW4J1gbJk+35Mmnk0mE7fddhu1tbVDCd8NGzakfKBVq1bx4osvcskll9DQ0DAikV1XV0draytOpxOTycS2bdu45pprxt3X0qVLefvttzn11FN55ZVXOO2005Ie32BQ7rAuWbIkyZofTo2NjbNy7LN13DB7x57quNO5wMr1RM6t2Wa2jn02XE9aY13scXVM+fF3eA9R5dGP2O/aej++UJSbLzsZL9tZXG7jUFfP1Bw7FlFmGZ8hZtTv1BtuqFuH/uAWytd8mvKKo+J+eQGLirVQmdrvyYwZ+xTKxvUElAKd6fL+JhIJTnjHy+cf7+J/N63GG2nmY6cs4rfvvslZy2r4/Jnz09rveKM7+j3+oqWCrz/8Pt+7fDH61r3UFWtZsqR+zO0ublNzen0xS+YPVxpebnbw2d++Q5ndyNKlS9OKczyLPIfY3uZM++f0v9vf5/wTKzjr5BoAdu/ejUbjxlQ6l3nFZvw7GjhtQRFLlsxJed9+k4NnmndPGJsvegirUUtVzXxqiiZXOfyBt4155dopPzfH+r1aE+vmvvfepchiwGcs44T6YkA5Hz/7ly389nNrWHLMpJWp+I/KIJ/q9bCqpgCzQUujv5m2Qf+IOP7+3D4uPmFO0vHqirw8tGtrWu9LNq4pObmetL4B234IF1wH6qQP6GdMTv5WOV8A5mNPdtx35lNXpIP6qY9v1Lj3tkDlCuW16t9CNEhZwTxG1M8GToPWNyjN9PvlG4CerSy2+mHequPbV28jWEpYsOqsoZdS/pkHf8zcky4H7eE5BF5bzKICoGZ6/A0e8ooX65KToWr8uDJ9PUn6m3zSSSdhs9kYGBigr69vaNK/VF1wwQXo9Xo2btzI5s2bufHGG3nyySd55JFH0Ol0fPvb3+aaa65h48aNrF+/nrKy8UvBb7jhBu6++242bNhAJBLhoosuSismIYQQQojZrCBDkwu6A9GhyQWP+Lez5/PNCxdhMWj5xT+vpsJuxB2cglYbsQj8ZAEcfOH49yVGiQ628IzxYhIGK5QtG7mwqE6ZnEeIMahUKh764mlsPGUOtz+3j1KrkfnFSuvFE+fkZ/TYJ1Tb0WvVvNvqwOmPjOg5f6xvXLiI0+aPfLz9hOp89FoNZVM8sSAo193j6fG8o93FSTUFQ9+rVCqWVdrY0620bGgdTL/VRnVBHh2O8R/hj8bi+MMxym3GlCYY7PeFKLLok684Bc5ZVMKfvnw6G06ewxsHB4Zed/gjhGNxlh9H0hmg3G7kzAUlmA+3iCkw6XAe83f01f19nLOwJOm+aouVHuPuoPR5HtK7GyI+cLbmOpLsiR3+XfL1K600kslmn+e+PcpEegDWciiYN3qd8hVw6E2lLUcmuQ4p/217ewr21aH0pj4eaz4/nHQG5X3q23N8+8wEbw9YynMaQtKK5+uuu25KDqRWq7nllltGvFZXVzf073Xr1rFu3boxt62urubRRx8d+r62tpYHHnhgSuISQgghhJitCsyZ6fHs8IdH9FQFWD23cNSxXaEp+JDialeSz3/5Enzp5ZxPoPKhEg2j8vfzX41z+IP5Tn6vUjOi4UBRvdLvUYgJXHpSFXc+v5818wrIN+n49ImVx538S0alUrF6biF7uj24AmHyTak9EaFWq1i3uCSl5OpkFR5Hj+dEIkGnKzBq0sX6MgsHej1AOYcG/cwtmnz7m6MVWww4/REisTg7O1xYjTrqS4fnaXIFItiMWqxGLb7w5N+bAW+YCvvUJ/HHYtRpWFVTQDAc48fP7uU/UB7/73ZNfb9uOHIDd/jvaCAcY0+XZ8TNgfFo1CoWlVvZ3ekedfNj1uo9nLjr2QWFtbmNJRuCLrhrFXytQUmonv7V5NvYKsHTlXy9qdC3F+aePvE6FSeCwaoUACw4P3OxONuUSY7b3jn+fXl7wDLFfY9LFinv13QSjyk3NCwT95vPtNw9uyCEEEIIIXKqwHR8k1yNp3XQn/QR7EKzDvdUTC7oaIbKk2DhhbD7iePf34dZNMWftbsDn76Yy0+dR2Mgn/ZjJx0rqodBSTyLic0tMnNCtZ0Kex4qlYo7N56EUTe53sDHo8JupNsVxOmPkJ+XerXtv6ydxz+dVD3lcRWa9Qym+aSJOxBFp1YPVdsesaDUyv5eL/5wFHcgQqk1vYn8NGoVhWY9/d4Q97/VysPvHBqx3BmIkG/SYzZoU6t49mav4vmIVXML2NvtwXO4mrjHE6QsA8lvu0mHMzCceN7e7mRRuZU8/eTO8WWVNnZ1uqc8rhmrt1H5m967W0maJaZ2Doppx92lTCr4zq+gZyfUjV2MOYK5FLwTNA+fSkdXPI/htf397Ox0wylfVMaQSa42WHiRkqA/3vPC2zP1ydjpWPHs64e8/Jy3o5PEsxBCCCHELJVv0uEKRIjHp/aDXXOfj9riiSvuCkz6qWm14WhRHv2cfy60vHr8+/uw6tkNt1XCQ1dCyDu5bVxtOHRlFJj0nFJbwNaWwZHLC0e22hjwhrj4p/IzEKP9y9p5nFJbmHzFKVRuN9LpCiiJ5xQrngGWV9k5f+kUV8QxuYrnaCzO/W+28P/9aTuuo6ppu91jJ08XllnY1+Pl0KCfOYUm1Or0J0MstRnodYfocQfZ0eEasczpV6rHLQYtvhQSzwPeMEXm9JLh6TpS+fz6AaXdRo8rSFmaCfmJFJj0I1ptbGsZZM285NXOR9SVWGgd8E15XDNSIqEknJevVyqeH9wAO/+c66gyy9cLGj28uBkWXgxa5RxtGxy/5Q3mYmW7TIvHoW+fUsk7hs1/b+Rbf9rOl+5/F8+CTymFAL//tJLszARnG1SvUaqrj7fNl7d3dlQ8e7qUFik5ljTx3N7ezm9+8xt+9rOfDX0JIYQQQoiZT6dRk6fX4AlO3ePkiUSClgEf85IkngvNetxT0WrjSOJ53hnQ8ppSITUb7X8OIsGRr8Xj8OJtcOgtaHwSTroKwh7Y+/Tk9ulso1ddSoFJz8lzC0cnnovqlQ9/hyuPWgZ87O12E5viGxli5lu/upp/Pu04+2mmqNKeR7criCswcY/nbLPn6fAEoxP+nvzlvQ4efKeNxi4PL+8fnmOp2x2kfIy+03UlFpr7vezt9qTd3/mIUquRXk+ILleQXR2uETcmlepxHeYUE899nhDFluwmngEuXFbGs7u6AehxhzLUs1uHwxdmb7eHC+54mac/6ObkeZO/yVJo1uPMQMurGaf1TWjfpvx7/rlwYAsceG74tQ8rby8suBCMNlh2KaC0hTn/jpfHv0ZYSsGb3txrKXG1gdGufI3hyYZOHv7iaZy5oJjbnmuFL7+hJMW3/TZz8dirYd5ZsP/Z49tXJlpt5M+FwCAEnFO73+Ph6QZrRa6jSJ54/uY3v0kgEKC4uHjoS0yBRAL8g8nXE0IIIYTIoKmeYLDHHcKk12IzTpzosRl1+CNxIrE0k897n4Edf4TBZqUPpLVc+RDR/UF6+8uWRAJtIAMfGP90NfzjO0PHoPUN+ONn4f0HYMstsPcpWP4ZOPEq2PmXye3T1UYXJRSYdZxSW8g7zcP/7zroC/O5Rw4AKvArFYUdziDxBBmZsFKIVJUPtdpIvcdzJmnUKmx5oyekOyIUjfHTLfu59VPLWL+qitf3D1cP9riCYyZPzQYtRWYD//PMXj59UtVxxVdqNdDrCdLtCmLUaWjqH67GdfiVVhtKxbNyk6+l30eXKzDe7pS4PUHKs9Tj+WgXLi1ny55ewtF4xlptWI06fOEYDW0OIrE4LQM+Tp47+Ypne55Orpmudnj4Cvj9J6F0CRQvhGgAFlwEPTvxhqLc93pzrqPMDF8f2Krgy28qbSSA/b0eQtH4+L9X5tLsVDwP7Ifi+jEXeYIRnIEIcwpM/OfHlvDKvn5eOeiEtV9R/r8jExMNutrAXgMr1sMHfzq+fXl7p77VhloN88+BPX+b2v0eD2/31CfY05A08Ww0GrnuuuvYuHHj0Jc4Dv5BpRKn8Un41dm5jkYIIYQQs9yRfp5Tpanfy/wk1c6gTN5lNajTr/RqeRXe/sVwxTNA7ZnQ/Ep6+8uW9m3Me+6aqd1nJAiRABzcAs/dDE9+DR67FsqWwbVvKY+nOg9BzVpYdAm0vp68IsfTDY1PciBeQb5Jz5IKG73uEIOHWwQ0tDl4aV8/4fz5Q4+8dhzuAT2V55MQ6Sq3G2l3BghF41iO6Ymca6VWA93u4JjLnt3VQ22xmZPnFXLGgmJeO9BPIpEgFk8oFc/2sSuHF/z/7J13eGPlmb5vybKqJcuWey8znl4Ypg+9DSUhhAQCgUAgCQnZTZb0wC+bTduE3Q27bLKEQCDUEEoYIBP6zFBnYHqv7t2SZat3W/r98anakrs9M4zu6+JidHSOdGzJ53zf+z3v8xRmoVXKuGrR5NRtBVoFjSYXEmBVTS6bjhr5zWtHgZjVhkaREVU837/pBP/1Rur2cm9gELdvkJyTUPwvylZSm6/ho6a+abPayJBK0Cpl7Gu3cu2yMvb99DIM41B356jl2DxnsOI5OAh//xas/iZc8R+w8FqQyeGaP8IV94LxMG8d6ubnG4+ccpYk/7elnpf2dkzuRZwmyMoHbSFIhEVOg0nYYbX1pbDbyMqfGcWzo0cUxZNQb3JSm58lxnLKTH5z7SLu3nCQQMFiYYXR+uHE3nPXY/DRA8mfs7aDvlwonm0dkws3ng7FM8DiL8D+Z6f+dSfKqa54bm5uprm5mby8PDZu3EhTU1N0W5pJ8Mz1sOWXsOtRMQGwto1+TJo0adKkSZMmTTIGA/Def07qJUr1KrpsyQsgE6HZPLq/cwSdImPiSi+XGTp3Cz+9nGqxrfRs6N4/sdebKTp3k+kxiUnTVOEKK3e+/JqYxPpd8I0P4IIfi/bdC34Ei2+ADJl4XHVuYpuqvTsxqKd9Bzx8Icy7mlcG15GjlpMhlTC7MCs6IT7YIcKweuXlscKzVUySex3pwnOak0+WQoZCJiVblYlEMnHP4+mgPFedUFQKBkPc9MjHmBxeWswuFpWJ1vba/CwGgyG++Kft/Muze1NabQDctKqSX1+7aFL+zgD5OiUHOqwUZStZXKbn3teP8fAHTQQGg9g8IqhRLZfh9IvC8+EuO68d6sbuTV487XX4yNcqTtpncNmCIjYdMU6r6jpHLWd3q4WafA1y2fhitPTqM1jxHArBaz+A4ACc8x1Ydgus+Kp4bvF10Xv7R/uPUJKt5KW9nTyzvY3drSehc/ztfxtWUNzVauHF3Z30u/x87/n9E8vLcJoIaQq4e8OBaGG9weQkQyqhLZXPs1IPgz6x4DydjOAPXG90MLswK/r4vLp8cjVydrVaYdF1cPjlib3n8dfh2KvDt/tdEHCDJl+MZRZ8Fg6/NLH3gOlRPAPUXS5CIqdyjDcZHN1iUeMkk3Lp96c//Wn0388//3z03xKJhCeffHJ6z+qTitMkzNn7GkEiFV/K1o9AX3GyzyxNmjRp0qRJczri6IZ3/h1WfT2lB99olOiVdFmnbvLS3OuiOn9shedsRUZUQTtu3GbRbjrgA1W4rblgPmz7/cReb6bo3kdIkoGkY6fwKpwKnL1iMpZdCp99cPjzZ92c+LjqHGj7CBZfLx4/fD5c9u9iot+wGTbcAZ95AOZcTv+7b0aVilV5GlrMLlZW53Kw08bcIi0Ng4WUhgvPXVYvWQpZWvGc5pShOFvJwCnoOV6Zq6Y1rqi0o6WfrQ191BuddNk8LCgR13OJRMLlC4sw2r18UG9mWYWeC+ryk77mpVMUhFigVXC4y87ZlTmsXyCKTo980Ey/y4/Z6Wd+iY5gMES3zYPHP0i7xc262jxePdDNjSuHz2uFSnvmbTYinDc7n396Zg8O78C0eDyDsMvY126lJi9r9J2HoFefwR7Pux8T96Lb34SMJIp4iYTB/PnYWvdz70238U9/2YNakUEwBC99cy1lOZPzMx8X5hMQSrSPaO9309rn5g/vNPDing4+e1Yp58wepzWty0SjW8Vfd7STrZLz4yvm0mBycnZlTsI1IgGJRNzzXb3TW0tyGCFvdtKnThid1BVqE7ZdMq+QTUeNrDnrPHj5zvG/XzAI7duFqGLAL5TvEaxhf+fIAlbp2VD/5vjfA0TBfsAjCvhTTaYS5lwlHA5WT+B3MNWYjoqFgJNMyuW4p556iqeeeopHHnmEu+++m6eeeopbb72VRx99dCbP75PFiTdh1kVw2S/h3O8J0/zWrYn7+EdIL02TJk2aNGnSpInHFW61nESKdoleNaWF56bxKJ6VUiwTLTy7ekVBNbc6NhHJnwP9TWLCcirQ9B68e2/itq59OErPn9rAJNc4lTvlK6F9p/i3zyHU45t+Bnv/Ahu+Bjf8BeZczsBgELd/MOrXXWXQ0BJzIpR7AAAgAElEQVRWZB3usvGlNZXsceVF2107LR4WlWZjdpyc3/9tj+2g2XxqtWKnObkUZavQn0LBghEqDOoENePzu9pRZkpp63fTYfFQqldFn/vZ1Qt48OazMWTJ2d7cP23F0wgFWgWewCCFOiXVeRq+cX4tBVoFvQ4fxrDiWhP2eD7aY2dWQRY3rqzgxd2JCr89bRbu3nAAo91LoW7mgwUjzC3S4vAOYHH7py3gMLI4N9Z7XzxahQyPf5CBieYdnK6YjsGWX8H1T4pOnBR0yKv5ivp9zm3/I1ctLubpr6ziiysruH9T/fjf09YBXvvEztdpSuhWD4VCdFo9rKrJ5dGtzdy0qoJnd06gm91p4m/H/dy0qoKX9nYwGAzR2Ovk4rkFqa02QBSep9tuw9Gd0o7ihNHB7ILEhZZL5hew6aiRUNEi0UnlMic9NiXmE6DSQ24N9BxIfM7WAdnlsce5NWK8NxGcJvFzTVcXxuxLxSL+ySYYBONhKFx4ss9kdI/nH/zgB+zfL1oWm5ub+fGPfzztJ/WJ5cQbQuV81s2w5ptQuUas8EXo3A2/XyaUO2nSpEmTJk2aNKMRGdSbjkz4JUr0qqg370T42+4OXt7bCUBrn4s9bZYxByvpFBn0T9hqow/O/jJ88bnYtkyVmJj01cPuJxLtI2aK4KAIvXn2JnjxK/BxnALZ7wJLC9aaq6Fj59S9p9Mo1N9jpWgx9DeKonN/M+TPhdoL4ODzcP1TULEaAKsngE4pi7buV+WJwrPZ6cPtH+RTi0v4oD+bUFTx7GFJuX7aFM/ewGDKsKVQKMT25n6O9zim5b3PZPaa9k7NCw34Zjz8syRbiV4tH33HGaYizmqjvd/N20eM3LiygrZ+N11WD6U5qmHHrK4x4PYPTrt6uCBc2C6Oe58CXSxwsEinJEuRgdM3wOFOGwtLsjmvLp+GXmfCImZbn5t3j/ditPso0J48xbNUKuGcWQbysoRl0HSQo5ZTqlehkmeM+1hpJGzyTPN5PrYRltyYUlEb4YRyMeWSXiQ7H+HeS3KZXajlnNl5NPY6E3c88Dy8cXfs8d6nE6835gZ46Hz4y3UiF2G8uBILz71OEaR806pKLp5byA/Xz+W9E73j7uIKOk1sbod7rpxHUbaKv+1uxxsIsqI6N7XVBojF5ukOGBzBH7g+ieJ5frGOwECQxj4vVKwaLrIcjfaPoXyVOLbt48TnbG3C3zlCbrUYv0yE6bLZiFBzgTj/6bZCGQ1LM6hzRTH/JDNq4dloNHLjjTcC8LWvfQ2TaQbSM09ngoMi1OboxsTJjrUNWj6EWZfGthUuFO0LkZWqjl1iVenIKzN7zmnSpEmTJk2a05No4fnYhF+iVK+icxKK5/dP9LIhXHj+rzePc/u66jEHK2UrMyamjg2FhOJZkz/cf7BwPux8BDZ+W4yrZprHroQdD8PcT8E/7xStue6wH2XPQSiYiydvkfj3VCmznb0ibGisyORQtAg694iJSU6VsNa45RWoWhfdzer2k6OJFe2qDGpazG4OdtpYWKojW5VJILuKUF8TNrePYChEbb5m2jye3zpi5F9fPpz0uV6HKIZP5rucZjiBwQC3vH4LrsAUKMl3PgrPfnHyrzMOirKV6E9CqN1oVBo0tPa7qDc6+NyD2/jh+jksLdfT1uemy+qlRD+88LymxkCGVDJtqt0I+eHXjy9w52fFFM+F2Yqw4nmAw112FpTokMukrJ9fxKsHYtdchzdAt83L8R77tKu0R+Pc2fnTeg7Z6kxqxmgxlQy9OvO0t9sY93W/rwny6kbdbbfmPF5a8RcRjBv2/q00qGmNVwMPBkSO1p4nxYJqx274+7dh3zPi+VBIXHsu/lfhd/vOv4/vXEMhcZ+NKzx3WDyU5ai4clExf7rlbLLVmVwyr5CXwuOhMb+uy4xSX4RGIeOH6+fwy38cpTZfQ5VBM3KYoqZAFFCnE6cxqcezwxvA5gkkdGaAsAY6qyKHw112YenVMs6AwfYdovBcvipRoAlhq404WxG1IXF8NR6mK1gwgkovxqKt26bvPcZC934hNDgFGJPzfSRQsK2tjWDwDGsBGS+bfw7/+A5s+DrYu8S2UEhc+NZ9GzSG2L7SDNHqGPmj6toHs9fD9odm/rzTpEmT5gxhj3HPyT6FNGmmDlev8DWehOK5dJJWG619LnY291NvdPBRYx9fPbd6zMdW6uUc6xml7dXdL9oF4/E7QSoDeRJ/x4IFIhUdJt6GOVECHujaC7e9AUtvFN7TuTUxVU7bx1CyjGCmRiTFh5XCk8ZlGv8kqmwFdOwQ55ab/DOzuAPkxKlFK8MT4c1HjaytFT6W8yqL8ch0mDqaKNGryNcq6J0mxXO31YPNk7xYH7HY6LCkbeumkj5vHwB23wTb0yMMDsDHfwBbJ/ico+8/RVwwpyDqU3wqUapXYbT5ePC9Rm5ZU8mX1lRRkatmf4cVuUxKlmJ4FNPaWgOXzCuYNtVuBLlMSo46MyHEMF+roNPiwe4NkKeJFZ53t1pYVCbUdJ9eUsLGA13RY+xeET743onek2q1AXDV4mLuvXb6CjB5WQpmFYzf3zmCXpWJ9TQPGLzlzzs41Gkb+wH9jWCYNfpuLh8GjRzmXgXH/gG9J8jP9OMNDGKLqMQPvchgdgXe8nWw40+w4auw8msxxW3vMRFMt+xW8d94Q4j9ztj//eJe097vpjzsMR0Jzrx+eTnP72wnNNZuK6+VwQwFBTnC033drDxevHMtd11aR446k2CI1N+LrPzpVTwHgykLz81mF1V5mqRBplV5anE/rrkQjr9Ou3kcXUi9x4U4s/ZiaPkg0arD1p6oeJZIxKK5ZZyq5w/+G979zfQqngFmXQKNW6b3PUaj56AQGZwCjFp4vueee7jrrrs455xzuOuuu9JWGyNx4i2RrHn7W1CyNDaZaNws/mjXfnv4MZVrYysh3fvg/B+J9tDp9utJkyZNmk8oD+1/iG+8/Q2MLuOw58weM7e+cStO/8xNetOkmVZcvUJV0jtxxbNenUlgMITDOzG1VUufG0OWnHteOsi1y0pRy1NmVw+jLk/BgY5RJqpPXwvN7yVuc/WCJkWAT+F8Ybkx91MzX3i2dYKuGKRxQ+x4H8JDL8L8q8W/8+qEn+FU4DQJ9fd4qFwnuvQiiuchBIMhLC5/1LsURICWIjODl/d28anFov32rIocujNKcHQepTRHRV6WArNzegooPXYvjnAxC2DLMSPbm0RhtKXPhVYpo2MStjFphmP2iIm/1Wed3Asd2yhCsArng3ninvTjZWm5/pQsPMtlUvK1Cv5xoJvrlotiSkWumg6LJ6naGcCQpeChLy2f0vMIhULcuelO9pn2JWyvK9QmhMTmhwMH87MUSKUSNHIZx3oc+AeDLC4VRbPVNbkc63HgGxgEwO4NIJGA0e5LKGKfDJSZGcwvSe0jPFm+vLaK71w6uno3FTmfgIDBS+cVsGHPONS+fQ1gqB19N6efXI0cai8SSuaHzkWy8dtclN0Nz31JWPh88N9sMtzEd47NF0LARdfDpb8Q9hpee8zyVCIRAXX2Ied5dCO8+v3UJxGxZsguF8pbYorneFbX5OIbGGRv+xivl85e3Jm5CdY6c4q0XDinAIlEwrLKHD71+w/Z1pjEK1lTML01I08/yDUMSuV87/n9DMaFtDb1uqhNofCvMogQYooX41Xm86v/uQ9HigXjYTi6xRhKY4B5V8dEBBALF4wnfmF/rBx4XgQrr/6n8R03XirXDVdtzzQ9B08fxfOSJUt4/PHH+cMf/sAjjzzCokWnRsX8lKThbVj5dfGHYqiNFZ73PQPLb0+e1Fq5Ftq2iVDB/mYoWgjaklhYUJo0adKkGRfvtr+LTCrjJ1t/Muy5ExZR5Gm1t075+4pk63SoVZoZxmWG4qViHDGRdkOEUqdEr6TLOn7PQ4vLTzAU4qpFxexssXDDyvGlqxdrZdi9gdSewF6b6AgbOkl09aUuPM+6BL74vBABzHTh2T4k/AZihefe4+GFgnPF9rzZQmwwFbh6x6/eqT5PWG30HBymeA6FQlzxvx+wtcE8zB+3yqCmOk9DpUFMOs+q0HPEX0hH/T5WVOUKxfM0WW302LzY43xQXzvYw882HiEUCtFsdrO21pC08OwNDOIfSHdtToRI4dnmT75AFAqFxqbua/1ItMrnz5uUNdAniUqDmtU1hqgFRK5GjkaeQal+5oq0R/qOsKtnF7/6+FcMBGOLOs99fQ21+TEFb75WwcFOG4Vh+w2NIgPfQJAbV1ZEVY+yDCkl2Ura+8XfoN0zwLwiUewtOMmF5+lGo5BFQ1gnQrY6E8tprni+5qxS/r6/a2whiR6rKBiPoVOnz+XHkCUXHU53vAPfOw7dB/gP178i7TsOz3wB5BredM9j2SU38C/Su2lZ+C2QKaB0mbBvOPGmKDwD6EpEZ3r8dev46zFbiGTXs8g9Vl8RtdvosLgpy03supJIJKxfUMTW+jGG6rlMWKU5wywrIjz+5RVce1Ypm48mUTZPp8fzczcLQaW2mF6Hjxf3dCTkJzT1OqnJT67wr87T0By2QfmH8tP8IOM5lL+bD43vjPyewWC4wB9eKFx9p7BNGwxfl2ztScZX4/R5HhwQi+0r74D8iS8UjYnSZWA6KsbnJ4P9z4oMudKzT877D2HUwvNrr73GDTfcwEMPPcQXvvAFXnkl7T+ckt7jIk0dILdWTDK8NqjfBAs/l/yYkrPESlzbR+LLL1OIidR0G8WnSZMmzSeIUCjEptZNuAIuGm2N3LPqHo72Hx02Ga63iCJPk23qi1HP7mzjsa0tU/66n0QOddrYuL9r9B3TjE5kMlS6bLgqeKy8ey8LsxwTstto6XNRZdBw2YIirlpcnFCoGAtSiYTFZdkcTKV6bvsYCImAm3hcvaBOUXjOVEH1uZAzieCZ0UhVaLN1pFDkNAmVzcLPCas1CCuep6jw7DSNL1wQQJElfBQ7d4vfVRydVg/HjQ6e3dmeoHgGoYT8zNKS6OPZBVr2B8rxdx7kuuVl5GrkWN3+BHXUVDFU8dzn9NFocrLpqInWPhfnzM5ParXxP5tOcP+mKVKXn2FEC88+G32ePkzuxDnKnw7+if/b939jeKHjIsSyYO6krIE+SVw8r5Db11VFH0skEspz1SmLUNPBxqaNfHnhl8lWZPNKQ+p5fn6WApMjplzWqTJRyKR8/uzE612FQUNbv1iEd3gDrKrJBTjpVhunOp8ExXPNm7cyP9vHhw3homsoBE9ek2iXEArB6z+Czl3i3igZ3Tam3+XHoAl/f/LnCP/cz/+ZzdXf55W5vxUF43O/x8EuO2vqijGcdXU0d4LKtfDOr8B4WHSHASi0IM0EjyV2Ts3vi4XgAT88dB4c/UfiSUTusfoKsArxSnv/cMUziMJrS5z/9Ocf3BYNEh2G04Q5pEsaJgoieHJ+iS7RzzqC2pD4u51KWrbCB/eBtoiucKDv7taYuKHRPILiOU8onh3eAL9pnUOzeiE9+euETcpIuM2gzBYZFACFC0CpE/eLwYD4DHQlicfEd5SNBUuzWOzInIFrbKYKCuZB10mweOxvFkGbX35VeJqfAoxaeH7iiSfYsGEDDzzwAC+99BJPPvnkTJzX6Ym5PmaQb5glFM9HXoGa80SaZDIiK3EbvgYly8Q2Tf70XUTSpEmT5hRlMBji6Y/FYO6Bdxp4dkfbKEfE6HZ18513v8Mf9/+R+Yb5FGuKkSKNTphfa3qNN5rf4ITlBAXqAlrsLVN+/v2uQLrFe4x83NTHIx9OU0HwTCNiOXHWzSJUp+VDeP+3ox9nroePHhAKk23/xyWSHbRPwBu3tc9NVZ6GsytzeOCLy8Z//sEBlpZksb8jRVtqy4diXOQcYp3jNo9uLTHeCclY2f04/HZ28t+zrUN4Nw89D+Nh8fmcdXNs+1RabbgmmNBetx4k0mEqor1tVhaXZeMfDA5TPP/iMwu5bV2sUJ0hlTCQP5/lyk4KtEoyM6ToVNOj3jPavDj9AwTDRe0+l587zqvhP944xgmjg6VlekIhYr6fYSwuP6/s64oel2bsxBeenzn2DA8feDjh+bda3uKQ+dCw49wBN+93vB/b0HtciGzy503KGuiTxFfOqeaCOYl/t+W56pRWG1PNQHCA15tf5+qaq7ll/i283PByyn3ztaLwF1Fn65SZbP3xRcOCDitzY6Fvdu8AK6pyWVWdm9SzOk0MvSoT61jtCE5VpDJuL+2KheuZjkLTO1D/dmyflg9h+x/h7Z+NyWYDROE5NyvxPkTJUlxzrmW/Ow++tQt3zXraLW7qCrVcu6yUF/d0iuv90puE7caXX4XMONW9riTWSWVpgUG/uFc3bhFFu43/QqYrTiDhMglPZX2F+Bn2Pk2HJebxHE9VnoaWcAdkl9XDrlZL6vGV00T3gG7ExabKVCGD6txY8XwKyfDZYMAr6lHaYrqsHmRSCbtaY+/V1OuiJi+50MCgkRMMhnjyo1ZW1Baxa8kv+LDgi8KWdqTuGHsXaIsTt5WvhPbt4rmswuEOArm1cPxVscAxMIZOq97wAuhMUb46LKCYYYyHxe+ucP7Mv3cKRi08SyQSNBqxmpGVlYVCkV6tTIrXDl5rbOAeKTwffgkWfn7kY697HG79B6wPp6tq8tNWG2nSpDnjONZj5ycvH6LX4eO9473sbBn7YKrB2oBKpuLxw4+zvHA5EomEWTmzqLcKNeGmtk08dvgx6i31XFp5KS22lik/f6vbT+ckAtrOJCxuP4c7bXj8gyf7VE5/XGah/J33aWFJ8cJtsPV3wwuufjc8ells0N+1D7b9XoT7+B2sCe7lqQ+O4TGOL+xOKJ6TBPyNkZLtP+fm7n9PrXhu3Qbzr0mueI4PbE5GpAVzrCE/Y6WvUYRBf/TA8OdSKZ6NB8UkoGBebHveLLEAMNnzG/CJz1epH/+xc66E6vNjCqMwe9osXLGwmPPr8inQJo795TLpsHCzqy69hIrBtmhLbIleSVv/1LaXBoMhep0+5BlSHD7xPn1OP19YUc7sgiwae11U5qkpy1ENUz07fQN0Wj3saZv6SfonHbPHjEqmwu630+vupdHaGH2u29lNq7012k0Uz77efdy/537xwGsTcyVdmfgbSFttpOSO82q4YmHx6DtOAUa3EUWGgnJdOWtL19Jqb6XD0ZF034hVRlF2rHg3tOgMwqc6Unh2eAMU6hQ89/U10fC1NMnRa+RYTnPFM5VrWZVxjC3HTDh9A6LorMqF+rdi++x+TCzAGg+KouEoRGyStEkWLioN4e9aThVHuh3UFWqRy6QsKNGRmSHhuNEBOZWw5ptQPMTnNrtUFDNBhNhVnSuCmnf+CWZdBEtuILspTqHr7BWK58KF0LWX0Os/wm/tSa14Dofdfhi23EhpJ+Yy0ebXpFQ8g/ibare4hy+cqqan8Cx3tIrrdN160BbRbfVyzuw8doXnZcFgiBazK8EDPh6JREJVnoaH32/iuuVlVBk07HEXAiF46yfwzA3J39jRI/yd4ylfJaxShgYLRqhYDTf9TSwiGIcvgA7DfHz6LTbiqVglCuczTe+xmBPDKcKoheeKigruvfdeNm3axL333ktFxfi8+84YzPWi2BwJk8mtFv4/Hbtg9qUjH6vJE6sR8vAfb7rwnCZNmjOQvW1C8bi7tZ9DXTbqTWNPQW60NnLNrGuozq5mTckaAGbpZ9FgEUW0BmsD9ZZ66q31XFJxCc32qVfbWtx+OiegGD0TsbgDDARD7Gu38ubhnrH5AaYZTigUVv7miZa+Vd8QnnirvyFSu+OxdYjBb2SS4rGIEJfDL0PNheT17+UXmU9gfyLFhCAFLWZX1Ot33LTvQN27n7z+vWT1JmlF9LtFi+XcK4crnl19oyueVTlCHTPVXWROk2jfDQ0Of21bEo/nrAJxrud8d/j5ZarF5zAZIqp36ajD+uHoy+GW4SrHPW1WllXo+cNNy7jmrNIkByayck4lUl1xNN9kUameA2MNVxojZpcPnTKTXI0chzdAKBTC7PSRl6Xgl9cs5Mtrq9ApMynVq4Z1nzi8A6yoyklb/EyAPk8ftdm12Hw2ej29CVZV73W8xyWVl+AZ8GDxJhZATG4TDn/4Pt57QniaS6WgrwR3H/jSIb/JWFGVS8UkFvPGQ6+7l3yVuI5mSjO5rOoyXmt+Lem+GnkGqsyMUUMCKwzq6KKT3ROYlO/xmYRelYnttC88r0PVtZ2bijo59vqDwtP3/B8JFfHgAHTvh4ZNcNmvxIJn/ELsENx+sbjY7xLBgskWLuoKtZwwOWjtc3Gw08bCcMilRCJhXrGOE8YR5hG6EnG/Pvg32PLvMPcqYe3QsAkq1kLthah798b2j3QV1V0Gdx3AW3khlysPoszMGPbSBVoFnsAgdm+ADxrM6JQy+l3J1ewDdiPdA1ryNKnFnRqFDK0yE6NjSA6HOnfC2R4JbLxLjAXDyO2tYJgNV/4XrPw6XTYP62rz8AQG6bC46bZ70alkI3YxVOVpyMyQcl5dvlgg6PeImtiRV8T3oGvv8IMcXaAdEgRbHi7cWpP4O4OwLitfKfZL9ppDmWnFc/ESMJ4Ea6neY6K76BRi1BHqr3/9a8rLy/noo48oLy/nl7/85Uyc1+mH+XjiqoJMIVoFqs+LFZTHiiYvXXhOMzM4eoRnkukYvPPrk302ac5Q9rVbOdZjZ0+bhVK9ir/t7kCjkNFgco65LbrB2sCcnDlsuHoDZxeKEIVZeqF49g366HJ28bnZn6NQXch8w3za7e0EQ1Nb7LS6A9i9Azi8p/nEYQawuv0UZyt58L1Gvv7Ubo71jH2R4YxmMACPXRV77LNDhiLmVXfBj+Dc74oC9KEXIRA3SYm2lIYXXTzhycrOP4mxStEilns/JsvTOaaJTCgU4ucbD7P5qIlF4cneuNn8C3oX3YHvgp/yb65fwUt3irC7CMZDwo5CXzk+j+d4cqun3m7DaRSeeflzh1sG2DuFkioeiQT+5QCUJQl4yasTE6HJYO8ePlmbBN7AICd6HCwu06OWy8jMGGNBu3BhVHG0tDyb/alU7BOkx+alKFuJTpmJwzuA0zeATCpBJc8gL0vBz65eAAirgvb+4Yrnc2fnc8KYLnaOF7PHTI2+RhSe3b30e/vp94prxJa2LVxQfoG43w5RPRtdRpz+8O87fp4klQpV29C/6TQzTp+nD4Mq1jlyUcVFbO3cmnRfiURCvlYRtdpIRWVc4dnhHUCbLjyPiRy1/LQPF6R4CVia+Rfrb6g7eJ/wTV58vVjgfOY6eOqz8Kn7xaLrTX+DBdcmfZl3jpm49L/fJxQKRQvPycjLUvDti2bz9ad28+iHzayoyok+N7tQS4NphOu9rkzcezfeBTf8BRZeKxTPIBaWy1ehshyL2Tc4TQmL3d0F53OpbF/Sl5ZIJFQaNDT1utjaYObKRcX0OZN/tj5rDyFNQTSgMxXxFjZRMtUQCkJgkt2WDZvg79+KWkLIHa2iI0tXAtpCuq1eSvQqblxZzh1P7uaXG4+wpGzkDquFJTq+sKKMzAwpVYaw9cjFP4U73oMVX4Gdjw4/yNED2iEezobZomNm6/+KbLRUlJw1vPCcrJus9zjkzaASWFssxozBGRbYnI6K54GBAQKBAIFAYGyJxWcqyb7EhQtShwqORNrjOc1M8cz18PZP4e1/HR6ikCbNDPHcznZ++sph9rVZuXVtJZuPmVhdY0CnzByzdUWDtYFZObOQSWOr73U5dTRYGmixtVCuLeemeTdx49wbUWeq0Sl0dLsmqTIcgsXtR6uQ0WX1jr7zGY7FFeCSeYW8f6KXvCw5jb3pYtCYkMqEUiSiWnaF1c5DUecKdWH3/ti2SEupRfio47EICwinEUqWwpp/ovfi/+aQdJ5oOx0Fo93Hy3s7efu75zOnSDv+n6WvEUxHsVWsR7P8Rm4Y+BmB/Hkinb5+k9ina6+YTGiLxHnGj0Md3cJvcTSmw+fZaRJeg0OLxqFQco9nAHkKBWPtBXDgucmdj70z+XuOgw/rzVGF2Z42C3OKtKjkw5VcI1K0GHoOALC4TJ/at3uC9Ni8FOmUaJUy7J4AfU4/hiSt/nOKtBzptidsc3oHKMtRYfWkFwbHS5+3j1p9TPFcmlVKk7WJHlcPh/sOc37Z+dTl1EWtrSKY3CacASeDwcHhk2BtsVC3JeH9jvd58cSL0cdH+47GlNNpphSzx0yeKnYPWWBYwLH+Y+IzS8L3LqtjUdnIC40V4YWfYDCE3RtAp0p7O4+FYr2SBpMT38BpbEGWkQnlKxmcfQVfk/yU0Jp/FuORK++DJV+Er78vCrwg7J2SdOkEgyF++9Zxum0eum1e+lx+DEP9neP48toqLl9YxG+uXcQ1S2P3wbrCrNEVzweeFUXmsuViW/FisaBduAAUWnzaqthieCTIOczRrJUsCeyDI3+HvU9D94GEl6/OU/PnD5sp1atYWJpNXwrF86DDRMYYwt8qDOrhPs8SiSjiT0b1HBwUBd9zvgP7nwVA4WgTBd8wXTYPxXol379sDp9aUkxNvobf3ThCERj4+vm1/GC9UBYX6ZTYvQFcUq2wSFt2i1A++4cU0u0xxXODycHtj+8kJJHAkhvgrJtER18qSs4SFnLRnysI9y8GW2ds22BAdGXNpNWGTCECEmdSVBocBHNDLHvuFGHUwvN3v/tdzGYz5513Hl1dXdx9990zcV6nH+Z6McmL5/qnYMFnx/9aaauNGeP15tfZ3n0SfHeG8G/b/o0m6zQEII2E1yYuSodfEi0sPvvox6RJMw30u3zsaO7H5PBx3dnlhEKwpCyb2aMNGsMEQ0Gabc3UZid6xdXqa2myNbHXtJdZ+llUZVdx64JbAVGUPtY3dR6ToVAIizvA/BIdnVYxkPIPBNMLtimwuP1cuaiYf7qwlhtXVtDYmyQ0Jc1wJJKYbzGELRZSFF/LVkLHjqjprhUAACAASURBVNjjaOG5Rfzf3S9aSwGKl8K8T6NacBUfDs6H5tELz+0WN9V5mgS/z3Gx/69CCZWRiUQiwa2tpmveV+CSfxMqbIgVnuUakUDvDSto/S7xXNmK0d8ntyam8p4qnEZReM6fEwsHDIXE9oxMMckYKyu+BifeiC0ITAR753Bf6XEQDIb42pO7uOXRHaI1uN7MubPHoCYfSsHcaCF+dkEWPTbvsJC/yWC0eynMFoVnh3eAPpcvaUFiYUk2R7qGFJ59A5TqVdjThedxY/aYqc2uxew1Y/fZObvwbJpsTWxs3MhlVZehlCmZnTN7uOLZLexxnAFnYgA7iOJCCsXzzp6d/CPOW/W3u36b8DjN1GH2JhaesxXZ5CpzabUnvx59ZmnpqCGBarkMnUoIBwYGQ6iSWBGkGU5tfhYLS7N5ZvvYg7VPST7/ZzTX3EeLtIL2s74vtlWsgsXXjek+teWYCYkEzq/L50CHlT6nD0MKxTOAVCrhrkvqOHd2foIdR12hlvqROlyyS8WYYv5nYtv0FXDXAWHfALjzl4oC6a7HxDUszpO6waXmSOGnxcJxw2Z49iZR8AtTZdDw9/1dfP38GgwaOX0jeDwXliaxkBhClUEzXPEMYwoYdPkGONyVogPJaRLF64o10YVjub014XrdZfVSqlchkUj45gWz+OHlc5NajKRCKpVQkauOBi6SVSBsVoZ6Hzt6xIIAcLDTxpZjJt45boIr/gPWfosj3Q5u+fMOklK4QAgaIsVsW5v4L35hv3OPGBMqJ9ilN1G0JSkXWqcFS4sQZiiShz+eLEYtPFutVr7//e9zySWX8MMf/pCurrQ3WlIsLeKLHE+GTEwSx0vaamPG2Nq5lX2m5G0yM0UoFOLt1rfZ37t/9J2nkvadYkJ//VPw2YdE6EuaU5Jv/3Vv6lCKTwD9Lj+fW1bG6ppccjRy5hZpWVaZI7zbRmmLPmE5wQvHXyBbkU2WPPEGq5VrubTyUn6353fM0s9KeG554XJ29OwgGAylHgyOA09gEAlQW5BFZ9hb9CtP7OS9E+lreTKs7gAVBjU/WD+X2vystOJ5PMRbRwxp/UygfKUIZIlg7xRtpNY4xXPlOvjco2LiAmSrMnk/MJdQ03ujnkZbn5vy3An6kAaDsO+vsPSL0U1FOiVGu08s2Ld9LFQqkcIzCGuLiM9z4ztQevbYJg9TrXgeDIgwabVBdLpFFM+vfg/+e974vQNVeqH+2fnIxM/J1hmdrE2ETquHbFUmFblq7nvzOB/Wmzl39hjU5EPJqYoW0GUZIuApZWjkBOixC8WzTpWJwxfA7PRjSOKLWVeURUufC28gVgRwegcoy1VjPd1b2WcYd0BM4os0RTRbm8lV5lKXU8ce0x421G/gmlnXACQtPJvcJiBceO5rFFk4EbTFKb3NI0rqgaBQ4Hc6O9ljTOIBn2bSDFU8A8w3zOdw3+FJvW61QcP+DitapSwdKjgOvn/ZHB54pxH/wGmce6HKQZKRyfKqXHa2jF+Ju/mYiWvPKmNJuZ79Hbaw1UZq/+NUVBk0dFo9CfeBBHRlootszhWJ2+MsUu2V60Vx9OALcNvrYhwSpt3ipv6s/ydsOq57TNRv6t+OPl+Tn0WVQc0VC4sxZCmSezyHQih9fSyYNXv4c0OoNKhpNicRaahyY9ZpKXjkg2a+8fTu5GKYiD1Y0UIwHQWfk0x3T7Su5RsYxO4JJA0SHQ+zC7Uc644TE1WdO7y7zhGzDWvtc1NXmMXvNjdEz3t/h5W9rZbkP4dMIcQAPQfF497jYny8/6+xbrnm94W13EyjKxaWaDNF77GZ9bEeI6MWnmfNmsXu3bsBOH78OCUlJQQCAfz+9MAtSigkggT1UxS8mLbamDGsPutJb9+LhK8026Y+7GxE2j6CyjVQvgJmXQp+x8z7D6UZlcBgkFcPdrNrAoO304V+l59vnF/DH28W/qcvfXMdyypyRm+TA36/5/c8e/xZ1pWsS/r8P5/1z/iDfmbnJA7qVhWvYkfPDt6r7+XrT+2e9M9gcQfIUctFqFXYHuRot4PtzZ/cz20yWNx+ctTC97E2P4umtOJ57OTWxBTPyfyEI0QKz5EBt71LKFrirTbUBlj0+eghUqmEbuUsggEPdIq/i2AwlLRY19bvpmKiheeOnaDQQtGi6KZCnZIeu1dM+hZ9Hl77gRhbRcKHsuIUksdfhzlXju29JlN43v9cTCEewWUWvzdphpjk9B6Hj/8oJjQ/boPb3xz/+1SdG1UaTYhJWm0c73Ewp0jLPVfN45X9XbSYXZxVMbJ/Y1L0leIzC3/nVlUbePe4aWzHGg/Djj+lfDoUCrH5qIllFTlhq40B+px+8pIonhWyDKoMGo6HveNDoRAu/wCFWgW+geDpXdSZYcweMwalAb1CjyPgIE+dR62+llebXuXSyktZnLcYCIf5WhsSshOMbiO5ylwcXqv4XuRUx154BMVzj6sH36CPeks9g8FBjG4je4x70h1E04DZnbzwfKRvcmFYtQUa9rdb0/7O42R+iQ6FTIppaIjcacjyyhx2tY6sxE3G9qY+VtcYWFKmF4rnUaw2UiGXSanIVaceXxpmwa0bowvvyfDmzoU73oHbXhMdPXF0WNyU58SNgVZ+DT78n6jF2WeWlvDsHWvIkErI1ciTWm34XFb8IRnLZhWP+vMsq8hhR3P/8Owb9chWGwODQf66ow2rO0B9Ms/ryPhBoRX//+gBPIaFkKlkW6OZh99rIl+rIGMUD+qxnP+etrjvQ/V5YtwUj6M76vHc1u/m9nXVOLwBtjb0AWKs4vAN0JtKMFS5DpreFf/uPQYLPw/BAejYJba1vC/GWzPNCNZS08LpWnjevXs3P/jBD1i/fj133nkne/bsYf369VxxxRWjHXrm4LGARCpUK1OBQisUNUN9b9JMORafRSgxTiIN1gZkEllCSviM0PYRVKwW/86QgUwF/rTq8FSjy+phMBhiX/vUhjSdSkSCQ2ThAKvf7v41nc5O1tbm8e5xU9R3NEJgMMBtb9yG2WNmp3Enj1/+OD9b+7Okr12kKeKRyx7hnNJzErbPzZ2L0WXkYHfHlKhtLS4/enUmZTkqOi0e7N4AZqeP3RMYdH/S8fiF+iTSfludr6HZPPYgyTOeeOsIW0fq1lV9pQidsbWLx/Yu4WUYKaR6+oVSZuhhWSrMC26Dbf8HwMYDXdz59HC1YbtlEorno3+H+VcnbCrQKTDZw5PtS38h1MylZwvrChCtmU6jaGWtfxPmXD6298qZYLhgKASbfwH7nknc7jTGfB6zyyHggsMb4AtPi/HbRNR9eXXQe2L8x0WYbOHZKArPeVkKblpVwZpaw9gDBeNR6oRvZ1g8ce2yUl7e1zm2Qu+ux+Ctn4AzeZfIjuZ+/INB1s0yoFVm4vAGRAt2ioLEgpJsDoftNtz+QZSZGcgypOhUmVNq//FJp83RRqGmkGyF6C7IV+WzpngNG67ewHeXfzeqZs1WZKOVa+l0Cj9N/6Afu99Opa4Sh6VJiGoy42x5tMUx+58h9Lh7WFG4gv29++n19JKjyCFEKPraaaaOVIrnSRee87PY22ZN+ztPgDytApPj9O9yXF6Zy+7W8YkvjHYv/W4/c4u0LC7L5kC7jV0t/SNabYxEXZGWfe0psgakUjEmmiDt/R7KclSxDQuuFarhJ66G7gNkSiUUacX4JS9LnrRz9XhDA/aMnDEt0JTnqtGrM6P3tSiqnBEVz5uOmijLUXHtWaW8fcQ4fAd7V2z8ULwEtv0ee/nFADz4biN/2d5GpWGCY704zq7MYU9b3GdRvhKMR8DniJ1HKCQW9hFddVV5Gv7pwln8fovopqk3OZBJJcMWE6Kh7nWXifEhiDFVwVxhZ7btdyJsu3PPpD7zCaMrmWHF8/HTs/D86quvsmXLFp577jk2b97Mli1b2LJlC5s3b56J8zs9sLZCzhSpnUFMWjT54E6rnqcbm8+G3X9yLSbqLfWsKl41s4XnwYHh/phKXezin+aUobXPjUImZV/7J7OAORgM4fAOoFeLQWUoFOL1ltc5bD5Mea6a5VW5bNiTONns9/azy7iL77/3fRYaFkYnxKlYWrAUeUbioFUmlbGscBn7TLuxuANYUoR+jBVrWPFcnita4Zp6XVQZ1BzqtBEYHL+6bn/v/pPejTFdCLWzPFqwyFLI0KvkdNkmmcp9phBfSLW1py48SyRQuiwWtmLvhPJV4v/BQbForsoZdliuRk5zxeeEasTWyVuHjRzrsQ9TG7b3D1H7jJVQSITxzEssPBfplPTYwoVnuQY++6BQI0WIKCQ7d4sxUk7V2N5PkyfueeMN3+lrFOqbE0MUzJFgQRAT1+8eha+8NUwNNS6yy4V9x0Tvwfau1Mr3MXDC6KCuUAREfu/SOdx/w9IJv5aw22gBRKtxTV4WW46NonoOhYSKvXR5zN97CE9+3Mqta6qQSCTolJlhj+fkVhsAC0p0fNTUx8BgEKdvIOpLq08XnsfFptZNXFB2AUqZEkWGgnx1PhnSjGFdRBCz27h/9/1sadtCniqPbHm2KDznVifurC0Wf8/dB6B1W3TzYHAQs8fMJZWXcKD3AF3OLoqzillWuIw9pqm12wiFQjOfr3KKMdTjGWBB3gIarA2csEx8Maw2P4uDnTa0irTiebzkZyno/QQUnucWa2nv9+DyxcQjTb1Obn98Z8pjPm7qY1V1LlKpBEOWgjW1Bs6qyOHKxaMrgpNx65oqfr+lHqdvYPSdx8HAYBCTw0uJPq7wnKmEq+6DuvVC9Xx0I/zxHPA50Skz8fgHhy3C7jt2gkH12PMUzq8r4L0TQ+6nqpE9nrc1mrl8YREXzytk09EkhWdbR8yqq3gxBFw4yi7E6RtgT6uFN79zHn/80tljPsdUzC/W0drnin0WmSphpRbxeW56V6igw4GTreGuuquXlNBt87K7tZ/jPU7WzspLKDxv3N/Fop+9xTUPbMVVtFLkVzl7heo3bw6cfau4x7x0hyh2jyeDY6rQlaS0lpoWTEdPz8Lzzp07+dSnPsWNN97I7373O1544YWZOK/TC2ubUBZNJWmf5xnB4rWc9OJOvbWeC8svxOQ24RuchoGG1w6PXSVU9BGsrSK1N94fU6FLBwyegrT2u7l4XgGHOu0MfgIVoRa3H50qM9rC1e/tx+F30OYQ4Sq3ravi8W0t0aLXS3s7+NmrO9HJdewx7uGC8gsm/N7LCpfR4hKqnuahSdGj4A0MJvjGWdx+cjSZLCjR0Wx2caDDyuIyPRW56mEhV2Phf3b/D1u7to77uNOB/rA6PJ6afE06YHCsxFtH2DpF0TIVRYuE313AIzpadKXi2m9rF/eGJB7JBo0Ck18OZcsJdO7l/fpevIHgsDbRtn43FUNVMMEgbH8oIWBnGE3vii6bwgUJmwt1SoxDJ9vx6uGixeLY468N92QciUgg43gDBpvfhYXXiiJqvCVAJFgwQpwf5ISRSsFQGwsqHA+DA6IYrp3YxBzghNFJXWFW+FQkqOWTUCnqK2M+4sCNq8p58L1GBkZagOs5KJTtn/of2P140l32tFq4aK5QmmuVsmhXSSrF8/qFRXRa3Fz9f1txeAfIUoqfKVudic2TtgscC4FggC1tW7is6jJAqJrzVam9v2fnzOag+SBPH32a+3bfR6G6EK1ci8PWOjwHR1skJuLbH0r4zCMK5+WFy9lj2kOXq4sSTQkri1byfseQtuxJcrT/KF97+2tT+pqnE6FQiD5PHwaVIWG7Tq7jxyt/zHfe+U7U43u81OZn4RsIphXPEyBf+8koPGdmSJlTpE1Q6L6yr2tE68Dtzf2srol9Hx++ZTn3XDkP3QQtW1ZW57JuVh4PvtswoeNT0WP3YtAokMuSlNIK5oPpCLRuFffmf9yFVCohRyMXPs8eC7xwG49tbaa+qQlD4diDgc+fkz88O0adO+LCutEuCuSranIxO328si8m5mk2uzjRcJyQrpRtjWZ2SxfB4hsYVObwYb2ZZZU5ZKsyJ/z7j0cukzK/WMf+eAV6ydKoNQmN70DthYDojLR5AhTplMgypNyyppLfbW7ANzDI2loDTeFOVd/AIP/55jGe/soqDBo5rxw0Q815UP+WGE/lzxFjtHPuggE/XP/kpH+OCaEtSdnhM+UEB6GvAfLrRt93hhm18Hz//ffz9NNPk5eXxze+8Q3++te/zsR5nV5YWqeh8JwvLlZppo2B4AAOv+OkF54brA3MNcylNKuUFlvL1L/Brkeh9cNYKBOE08WHqFWUunTA4ClIW5+LRaV6CrQK6k2ntgJ2IDgw7g6CiM1GhIjXeZtdFJ7X1BgIhULsbrVgdvr42d+PUG82UpdTx09W/4Qrqidu+zQnZw6WQCtLyrJpHmfR8z/eOMZ/vnEc38AgX/zTx/TYvOjVchSyDBaVZvPCrg5q8jUsq8yZkN1Gn6ePPk/fuI+bKX6/uZ6vPrGL32+uH7uHa5iIOjwe4fOctvoZE9pikcTuc45stQGiWNtzQAx4tcWxAmfHLpF2nTG8KJCrkdPv9EFOFW2NR5mdr+HavA76tj8b3ccbGMTiFpOCBKyt8PoPRZhLMvoaYcMdcMV/DbOkKNQpMdpG8LVceC0YD8Hep6FunH/38b7YY6XpXZF/UHuRmMREiLfamEry5kzMbsPZI1pTMyY2MRwYDNLU62RWwRSln+dUJvhif2ZJKTqljP/dXJ/6mBNvCM/uvNkw4AVX4rVvYDCI2emjKFt830ThOeLxnFzxXKpX8eKda2nsdWJyeNHGKZ6t7rTieSxs795Oua6ckiyhhstWZA9Tx8YzWz+bF068wJycOdh9dgrUBWTJs3A6upMUnsOK5+b3Eqxwelw9FGmKqNXX4hnwsMe4h5KsEi6vvpxtXdvodU+dKKfb1Y3JbZpwcfV0x+63R5XsQ/l07afJU+Wxr3diAeylOSrkMmna43kCFIzRauN0KE4vLsvmQIcoNIZCIV492I3dO4BvIPnidIvZNXX3ojA3rCjng/qp7SI32n0UZiuTP1m4QBSeO/fAZ/8Ibduh7WMMGjlNZidd256Fwxt44O1DfHdNDuqcsS8ar6zKZX+HLVGINIri2Wj3UahToJBl8PCXlvPzjUdo7RMCmWv/sBWXqY0eDPz5wxZufs3PkVX/AcA7x0zRxd6p4uzKnMTAyeIlovAcCokxV80FgBA2lOWokIZFSZ9bVsZHTX3UFWqpydPQFA5ZfG5nO7MLtJwzO49b11bxl+2t7DR8hsBrd4NMGfPvXvst+OKzwg7tZKBLHaY75VhbxZjwZP2sIzBq4VkqlaLX65FIJCgUCjSaKVB2fNKYymDBCMWLRfhOmmnD7rcTInRSPZ6DoSDNtmZm6WdRk11Ds32KAwYDHvj4QaFwi/cWMh8Xq4DxpBXPpyStfW4qDWqWluvZ15bCp+wU4R9N/+DuD+4e1zF9Tj+5cUXIFnsLxZriqOJZIpFw/fJynt3Zzt0bdrOmVofNZ0Wv0HP9nOuHKXXGQ7VuNgFZJ+fMykueFB3m6Y9b+cXGI1HVdSgU4s1DPbx3wsTOZgvbGvt47VB3NCxvVXUuBztt1ORnsaQsm0Od4/fnNnvMp3Th+YN6M3WFWfS5/Nyz4eC4jo2ow+MRiud04XlMSKWiiGM8LHz94tW3Q4konuM9gAvmCyVOEpsNCBeeXX7QV9LfWc8t+fX8yPFrZn/4XeGRB3RYPJRkK4eHzZiOQnYFvPNrcf8Zygf3wco7YPYlw54q1CkwjhSoJFOIY0F4P4+H3Bqx4DpW+hpF6E3N+aJN851fx0IZ4602ppL8ORNTPI/DZiMUCtEwZAGzpc9NUbZycirneHKqEhTPUqmE31y7iCe2taQ+pr9ZhEhKJMLvesjvodfpI0ctj/pO61SZ2D0Bms2uxFbnIUgkEoqylTSanDHFc9pqY8y82fIml1fFvNRzFDkUqlN/9+ty6rD5bFxVcxWfrv00JZoStHItdrdpeOFZrhat6R6r+HsLEyk8SyQSzi48mzda3qBEU4JOrmN91Xr+fOjP7DFOjeVGj0t0MrTaW0fZ85NJr7t3VAX7RK1IMqQSqg2aKVFKnmmMRfFs8wS4+L53CZ7igZuLy/Qc6BBj4BNGJ27fAIU6BWZn8q6TLqtnxGv6RJhfouOE0YF/IMhgMMS/PLuXzcksJ8aBye6lUJt80ZOC+dBzSIzRylfB+T+ELb/CkCXnng0HMW59CoBVuW5yQtZxLWSr5BnkquUiiDm6ceRwQZPdS4FWFMnnFeu4clERbxzq4dUD3dyyporKTCu7+lXsbOnnny+axc/+fhiADxvMnDs79fVhIpxfl8878dZbRYuF3VLPQaFMDluotfW7qYzLEMnRyLlyYZEoPIeFKqFQiCe2tfCN82sBOGdWHg7vAD/Ym8ftwbtpmv/NKT33SaEtFnUYv2vkjsCp4BT1d4YxFJ4rKiq47777sFgsPPzww5SUlMzEeZ1eTEfhufYiaEj7aE8nVq9VtABOl+LZMfpNze6zI5PK0GRqqNRV0mqb4sFv8wcitbdidWKaqvlECsXzJzfA7nSlLexxtbRCnzog4xRhe/d26i3jKO4wXPHcYmvhvLLzaLe3R7ddu6yMv+/r4qDvzyyafxDHgA29YvJhrm6PCqlESpHBN6zwHB90984xE8/vauepfRb8A0EOddrJlEnpd/n568428rUK9rZZoyreldWiGF6Tp2FesY4j3eNb0PEMeHAGnPR5T93Cc7fdwxdWlPPDy+fQ5/IP8/8dCavbH/X0jiAUz2mrjTFTsQYOPAdZRSDNSL2fvlL4Bm9/CKrWiW2FC6Dlw6TBggCGrHD6ek4lSkc7dcF6mss+g02WF1VsHO6yUZufRJVkOgwLrhFFpkiyeITgoPBLXnx90vctyhYezyN+l1bfCTc+F/UAHDN164UKe3AMPo+uPhEOdOkvhR1AzQVCLfPyneJ5Z4/oSptqkhRcx0S8P+MoNPa6uO6PHyVsi/d3nhL0iYpnEOpjbyCYYE+UgC/O9iXJ76HL6qU4rhihU8rY3WpBq5RRnTeyIKZQp6Te5Ix5PKvlacXzGAgMBnin/R0urbw0uu1X637F2tLUwUzV2dWoZCouLL+Q7y//Pt9c+k10ch0OT7/otBiKthjmXgmD/qhir8fVEy1uryhagcPvoDhLKAJvnnczH3V9xFff+ipmz+QVjEaXGKefqYXnZP7O8dTqa2m0NaZ8fjRqCzRolWmrjfEylsLzpiNGVlYbkE4kzHYGiVc8v7CrnU8vLSFfq8Cc5OcLhUJ027yUZE9t4Vktl1Geo+aE0cH9m07wjwPdHOqcnNDKaPdSOLTjK4KuRNhbZpeKufWSG8Fp5HrvBnLdzdQGWzmcMZ/LSv3jun9HqMhV09YX16WhTq14DgZD9Dp9FOhiRfIL5xTwznETx48e5Msd/0r2YB+P7PeQq5Fz+7pqDnXZaLX68Q0MUps/tYLTFdW5tPS5MUYK53mzRRfZjodhwWej+x3ttlMxJLz6nqvm8e2LZ1GRq6bH7uWxrS1kSCWsqBIiCqlUwl++uopXv30ud954HZ/fs5DPPbhtbOHG040qBwZ98Ns6OPi36X2v3mPDxYWnCKOO3H/+859TUlLC8uXLUalU/PKXv5yJ8zq9sLaK1sKppGylaD1zpQMGpwurz0q5tlwon6d6xdjaDv+7ZNRVLWtYuQlgUBmw+KY4QK7ngAiX0g3xFjLXi8ldPGnF8ylHKBSK+qguKTu1C8+hUIidPTsxuo3jalvtd/nIzUpUPK8uXo3Nb4u+Tr5WwY+urGJAuY9+Xw8ZGR7UssmHQ7T2ucmSVEBmd7RtC4SNxG3h8JNQKMSBThtP3L6CY70+LrrvXf7zzWNcvqCItbPyePVANz9cL27wkcLzsko9enUmNfka6gq1NJtdKdsKkxFROp+qiudgMITR5qNQJ1SSEgm4/WP7+Z7d0Ua7xRNVh0dIK57HSe2FYvA6ks0GiAJt0SJoeg9Wh9UfhQtFYW8MiudsXxcF7iaUJYswkhu9j7ywq4OrlyaZLBmPiMJ26dlCxRJPxy6hFE4xXlLLZWRIJbhG+i7JNVA2gZCbitWiyHXkZfG490RyRTZA1x5RJDv71ti25beLQN6ARyhzCuaN/xxGo3CBeI/xqmHsXaAbm0ekyeHF4g6IzzeMKDxPYWtzTlVMHR5GIpGQlyXH7ExRTPHaYoE/SQrPPTYvxXGTfK0yE7d/kKvGEDhVnK2kweQkKxxylq3KxJpWPI/Ktq5t1GbXUqQpim4rziomU5pawSrPkPP259+mOKsYpUyJOlNNllQuOgtzqocfoCsVCzu51VErnB53T/Q9VxSKAOwSjbjW1Oprefmal6nOrp4Sy42IurrF3jLp1zod6XR0Jny+Q6nNrp1U+OLVS0pYVZN8gTNNavK1CnpTXSvDvHawm6sWp/7sThVq87Poc/rZ3Wrhb3s6uGVNFXlZiqT3gj6XH5U8A5V8hMX0CbKoLJtNR408sa2Fb100ix775MKse+wx66dhSCRQOB9KlonHGTL40suc793Es7J/4/icr3PEl8eKHI+YjxuGB7WORFmuinZL3DxLlSu635LQ7/aTpZChkMV+p2tqDRzosFHiPIgeB91XP8v+bg8rq3JRyTNYWJrN8wetrKjKjYaATxWZGVIumJPP20fC4jxpBhTMJ7TvLzwfvAAQwobHt7Vw0+rEsWKBVklxtrDwue+6pfzXm8e5eXVlwjmW56rRKGSsnZXHjnsuptfhS/xdnSwkErj693DWzWKMOZ107h6WoXKqMGrhWSaTceONN1JUVMSXvvQl5PLkIR5nLMdeFYP+qVY8y+RQdc5wxVCaKcPis1CgLkCKdOpC/d7/L9jxJ2jcDAOeUQMi4wvPOcocLN4pLjwbD0HhonCLRxc0bhHFit7jwk8ynrTH8ylHr9OHQiZFp8xkXrGO1j53NeyWKwAAIABJREFUQjr0qUSHo4NQKMQs/ayoT/NY6HP5MQzxeK7JrqEsq4x2R0z1nFtwDFmGDJPbhErpRcbIq/CBwWCCivnRD5t5dkdbwj4tfW6KlNXYQ2209rnoc/p4flc7T37cyuEuof432n0EgyGWVeTw68uK+e/rlyLPkPLZZaWcOyuPvCw5n1tWRqleFbWPUMtlbL/nYtRyGcrMDMpz1TSYxl5UNXvMyKSyKVF0TQd9Lj9ZSvGzgQij63clb5uMx+r2c/dLB/nzh83DPJ5LslXYPQNTnjz+iaXqHPA7Ri88g0gJP/e7Ma+7grmAZMTCc0TxnDfQg85+grzapbQO6MHeRXu/m8NdNtYvSDLpNR0RbabFYW/peE68PmoooPCXnqbgt3Xfhp2Pin+/8k2hGE+GtW14cTxTJZQ5je8IQcB0tDHmzRYtt+PtdrN3jtlqoy/8u42/Hk254lmTl7Ttd0QVn9cmFr8haeG52+ahWB+b5Efa969aNHrhuSiseI4oL/VqYdORZmReb3md9VXrkz8ZCqUcL2YrEgNLtbZOHGq9sNYYymf/CIuujwamHjYfZlvXNsqyxHWtVl/LpZWXUqZNvM7lqfKmRvHsNrK6ePUZq3g+1n+MOTmplXE1+hoarA0TFudcvrCYtbWpFdVpklOgVdBrT207ZfcG2N7cz8XzpsHyaYrJkEr4+WcWcMPDH3Hu7HxK9Srys5LfC7qtU692jrCoNJs/vNvINWeVsrAkm56R8iTGgLCvSGG1AVC5Tlh1RcguRf+dHch/1EjdNXeTW1pLMSboSyIEG4XyHDUd/UMUzymsNpIps9VyGcurclll8CApPZvSpZeQlyWPLhKtqTHw/9l77/C4qnv7+zPS9K5R75KbLDe5AjYYg2NTAiQESCAQUi4QEm5IQhJuKAkhN/Um+YXcJJA3DUh8IbRAKDHNDhgDLrjJli1LtiSrjer0Ppry/rGlkUaakWZUXIjX8/gBzWn7nDlzzt5rr+9ab7e4WVUxM5NGGxfk81rdiMDmwhradMu4720xDrv72YN854rqcfslVywpZNt/XcRN5yYXfkozMyjPVtNm9bL9WB+/39Y0/ULDdLDkU6IP3J2ePWFa8Fqh+Z30ArhPIlKuVXzvvfdmsh1nJrrr4KWvwmeenxkD78p1cGL79O/3LABhtZGlyJo+u41QAHY8Ajt+C8e3iM+cneNu4gg4Yp30LEUW9sA0K1q766BgkVA8u7qg9ml44XaxTDOqM6gwnFU8n2Zos3gpzxYEq1yawfxCHYc6HUQiUX7xegONPadP2ODu7t2sKlwlFDIOoZB5YlfrhD5qI602PAMeuj3dlOpKKdWXxojngcgAfzv6N26ouoFeXy8yuY/M6PgKvVfrutnwy238ZusxIpEof97ezDN72uPWqet0UGWqotV5nM+vqeCmP+3iZ6818LfbzsMTCOP0D3Cww87iEkNsRv2cShN//vwq5hfo+eiSQn7xyRoyMiQ8fNPyuCTukeqCBYV66rtS/676ff3MNsw+ba02uh1+CkcoPbLHUzKOwM5mC+dWmphfqKN4lIdfRoaEihzN2YDBVKHKgqJlqRHP674tiOchyDVCYahOYrWhUWBxB4gq9ASiUmSudrLKFtI2kEXE0clLtWY+VlMUm3iIIRQQFgs586CgRhDPtlbY+7hY3pAa8WzxzFBgUvka0aZIREy+tiTpXznahU/1aBSvhF2/E2ru8exNpoKV/wF7Hk1vG2dnyqW6Q7/TkdUFDd0uqgqmsQ8r18GAZ4ytSU4SsgFIyWpjJCGRo5XzwJULmJsCYV5gUNLnCoyw2pBh9yae3GjodqVVnfJhhcVn4Z2Od5KH9+58BJ64LqV96XoacKmSWGNp84Qa0DSb5u79fGnLl7hp/k2sKxWEjUQi4ZcX/RKlNJ44mS7iudvTzbmF585MsPcZgKPWo8w3JZ9Ey1ZmkyHJmLa+iDPoZGvbVsxu88Qr/xsjR6vAGq2l15M4uPlYj5vZuWeOf/Y1y0v4xSdr+OZGQbDm6BIrns0OH0XGJCriKWJJiYFgKMLn1lRQYFDSNUXiucc1jtUGwEe+C0tvjP8sUwpSOQaVjI+cuxxJVy1IMkCTXlZNmUmQqTEojeC3iwnBUeh1BshL0M4718/h/NwAGEqQSCT85JolbFggJjJWz84mEhXjnZnAhup8GnpcNHSLcVF41Re533sjC4v03PfCIYLhCFcvnXgyPU+XIGdkFMpMatqtXl4/3M3/e6Nx/JDjk4H8xYKbmSkC/NBzMHdjUmHJqUbKxPNUZwgikQgPPPAA119/PTfffDOtrfGzy//617+49tpruf7663nmmWfG3ebw4cOsXbuWm2++mZtvvpnNmzdPqW2TRm+9UBKVrpqZ/RcsEmWrZzEjsAfsGJXG6SOe618WJc0yDRzdLEgB5/gJpo7gsFetUWmcVsWzJOQXA+jsucNWG90HhUdm5VpR9jESZxXPpx1ODAYLDmFpqZG9rTYefa+FzYe6uOEPO9lzInmgxMnC88ee51f7fsXlFZczyziLJrvwBHyltov3jo8/YLGMIJ5/X/t7Lq24FFmmjHJdeaz89We7f0auOpcb5t9Ar7eXTKmPSHh8xXNtu53Pri7nhQOdfP/lw2gUUo71urEMdnaj0Sg7miysq1xAu6udb11SxXmzsvn9zcuZk6elIkfDiX4PhzodLCk2JDyGXinjoioRCrK01Jg0nKu6UE99Gj7P/b5+qkxVWHyWUzs7nwRmhy+OeI5ZM0yA945buLgqjxf/8wIuWzRWLTs7V3PW5zkdnHO7sNyYCIlKJfMXTmi1YfcOYJbkIcmeQ4ZMgVWaw4Ctg26Hn1mJ/J37GkT1l0wpFIxeK2z9Prx2rwjZ8VqHS0+TINV7aVJQZQlVbcdu4Snb8k7izr+9HYylYz8vXiG2KT13ZtoHsOhaaH1/3KCgMXB0pmy10e8OoFNKY4rnQChMh83HrJxptNrIyBBijFET2eOWj/sdw8RzVgW4umNBliAUzyPLmqWZGfzHBQmsGxKgYHDQPTJcMJnVxpef2Muj755Iab8fZjzb+CyXlF9CljLBM8LeDm/9JN6+bRzoOvfjko1PJkWzKvlZzzvcuvhWrp9/PdKM8X2Bs1XZUyZDw5Ewfb4+zik4h1Zn62n5rp1JhCNhGm2NVJmSK54lEgmzDMN9uqnizq138qOdP+KphqemZX8fVsilEmT5z7OtfUfC5VZPkGztOGrb0xAfX1pMxaAfv7DaGPuen4lgwSHUlBj58+dWMjtXK/IkxlGUp4Ke8aw2UoGhRGRtpKl2BmEn0W4bYRUilYNUlVA81pMkBHFVhYmcSH9s0nrjgvzYRMayMiPnl6mpLpy6peEQRvIsSlkmn19TwR/eESKl9xzZOI3z+eYlVbx+uIdbL6gkYwJCOVUM+WE39rj5yTWL+fO7LQyET6HnsyYbFNoxORjThtonYdlNM7PvaUBKxHMkEuEPf/jDlA60ZcsWgsEgTz/9NN/85jf56U9/Gls2MDDAT37yEx599FE2bdrE008/TV9fX9Jtjhw5whe+8AU2bdrEpk2b+OhHPzqlto2Buw+evx1OTKDytp2IpW/OCHKrhUH4v1ln6GRhyOZCL9fjDE4D4Vr7N1j+WVh+syg1LloWC2JK2gb/CKsNRda0ejwrHM2CdJbKQVck/POsLbD+u3D9/yXY4KzH8+mGNosnpngGuGxhAX/c3sz/bj3GY19YxXeuqOYXbzScwhYKAveXe3/JHy/5I+tK1zHLMItmRzPRaFQEVFjGJxKt7iDZGgVtzjb+cfwffGOlUGbONs6myd5En7ePzS2b+enan5KjysEesBPNdBEKjt/hq223s7E6n59ft4S/7mzlkytLOH92Dm83CPubE4PBHDWFZfR6e8nIkPDgxxayolzM8FfmqGnp93Cg3c7ikrFqrSfqn+CJ+idSukYLivTsabWlPLDt9/VTrC1GkamYnmfTNEMonocHB9kahbBmmADvNfVz/pwcMjMkCX3jZuVq2dVijU0OnMUEqLleTH5PBss+K0KMEyBbIycYinC024VFWiDeZ4BTlk/Y0YHOXk9xaFRpejgEr98HiwZVkBkZgtw+8pIYWL1ylwj4myAUMGbzMVPIXwCH/wElq4R9Rl+C56ejHQwJiOeSleK/MyU2AKFGzxa2AykjHcWzK8g5FaaY4rm5z0OpSY1cmmZY40RQGsaEFSe12hiybRiy2siUioDCEdegy+GftBIuf5AcGFI8G1SJwwXDkSgdNh9/2t6Myz8JKw6/80PRX49EIzzT8Aw3Vt+YeIV9f4HF1wkruYnO19GBzmfHxfgq8sc8x+kOubhxfpJjjkKOcuqKZ4vfgl6uJ0+dR2ZG5mlbYTRTaHe1k6XMGmONMhqzjLNiVWxTRYujhduX3E6LPXU7tn9H1PXXgdRBpzPxPWkbFcp9piHZJGSXw0+OTkyKTDekmRkxaxKTWo43EE4edpsCehx+8nVTIZ5LYcCbtr8zJFA8A6izEk5Y9zgDyZXZjg7hsz8KCmkm37m4YEI1cao43H+Yi56+KC7/5zPnlvPG4W4cvgHePNLDlUsKOX9ODretreTqZalZh6WC0sFrdazHxdq5OZRmqak91XlJBYuH7Tbe+3Xqor+jm2NZCDG4uiE4OM729IOlCSomOS44CUja02xvb+eOO+7gwgsvZMOGDVx++eV88YtfpKVlci+LvXv3snbtWgCWLl1KXV1dbFlTUxNlZWUYDAbkcjkrVqxgz549Sbepq6vj7bff5qabbuK+++7D7Z7m0lx1tgiief624RLRRLC1iHLVScAX8rG1dQIfP022GBhNYNdwFpODPSCsNrRyrQg+mSp6Dov7ZtVtcNOzguyd4LuzB+yxTp9RYcTun76HodJ+TKjmAfSF4DKLwCRpklnys4rn0w6tVi/lI1J9z52Vzb7vbGTXfR+hPFvDVTVFtFt9p/Ql2u3pRpYhi5VrzjYKq402qxdfMEzLOMRzNBqlpd9DkVHJEcsRVhasjCWszzHOocneRL21ngXZC9DJdUgzpJgUJjyRTgLjEM+hcIQjXU4WlRhYUW7i4RuXc/3KMtZX5/FirTmmdj5vlokcdQ7WgHVMR7cyR0N9l4v9bfaE5WbbO7eLwUEKWDM7G08gNBymMQpvHO6OI2T6fSJpPkeVc1oFDLZbvXztqf2YR6kPs7XymHdsMuxosmDzBFkwjoJi3bxcDpsdfOKR96etzWeRBPMuEe+rBMjIkLCwSATxmDULoHw1AG5FHhKnmQ09j7Kg+bH4jXb9f5Apgwu/NfxZ4VJY8DE454vQvguqJhYJZGvk2GaSeM6rFgGDuVWCtG95Z+w6yRTP2XOF3UbJDBLPwLGBHAb6UyR6wgNisKGb2OsYhOL53FmCeG7scfG/W45NWtXU4mhha1uSfmw6xPOAFzLlYpJ8CFnlwmt7EELxPDklXOEo4jmZx7PZ7iNHI2fNnBye35dmvzsahT+uT3w/nWEwu81kSDKYl5VEidfXIDzmJZnDA95k6GtAlzN/3KrCzc2beaFvD7/vtiCTpGZhk6vOnTLx3O3pJl8jiKiqrCqOWo9OaX9nGiay2RhCsbaYLs/4IppU4A/58Qx4WJG/ghbnWeJ5PGxp3YIkKucvuw7zyzcbxyy3nOHEc45WnvBd0Gn3scv1B/5+7O8zevyMDAl5esWkfZ49gRDBcAS9avzKjHExRPjmzEl70zydAqdvIJ44TxIwKCxBkoz7nZ2p2bVNAZFohB/t+hHhaJgOd0fsc4NaRk2pkV3NFnY2W1g9S4hS7r9iwVgbtymgzKTmQLudSFT0QdbOzWH7sVOcn1OwWFSgeyzw5ndh/6ax67z9U2FPN4S2nfDMzXDgyfj1/vlN2PsX8f8n3hX9+swp3JczjKQtu//++/nmN79JTU1N7LMDBw5w77338tRT6ZfIuN1utNrhUr7MzExCoRBSqRS3241ON+zTptFocLvdSbdZsmQJn/zkJ1m0aBG/+93vePjhh/n2t7897vEDgQDRaJT6+vrUGqw5D9kFD1H+5h10OSJ4Clcj9fWjNb+Hu3A1IXUeZZ1H6DeuwZvqPkeg0dXIIy2PUOQdX6VSqinDuvd1PIVr0j7GSPj9/tTP/UOE8c67vb+deZJ5RP1RjrYcJduZnsfSSEhCfuZ5rTR0OkAiOtgGD6h7jtI1dPxoRHg5jcCJnhOUqkpjbYxEIxyoO4Aic+olVFmOdvoUOvoH9z1PpsOlKhtuzyioe23k2LppO8Pvk5Nxr6f9PJkkjnZYWFsI9fXJB20fnaPif1+t5e61eTPalpEYeY1323ZTKh++h/1hP2aXmdd3H6GmQMnBbi91h4+QmSGhL9BHjjwnpnY92udHJgnj72ujsa+RiDcS208oHKLJ3sS2o9vIi+bFPtdl6OilF3OXL+n1b7YGyFZl0NlynE6gUgrmVjtzFRG6LA7u/r/3aOgP8JHZWo43HEebqWXnoZ2Y5IJg7vZ3U2d/k4P1F5JVsJX9RxXkK/Nj5x2JRjjQfYASVUnK98AXarQ88EItRVjjVATRaJR7/97G55aZ2DhHvAdP9J1gFrNQRVXsa9hHQH/qFcB+v5+ntxzgxQM2Sg0yrl9sHP6uPHaO9Yepr09MGNb1+Pjh2z3cfUEeDQ3JB/dq4HsXZnHjM62nzfvqw/Q8SQfF6jD/PNDO4oKPUaPNg/p6bBiQ2E8wfyCIxKym/siRmI1H8ZGtuEovxtkwPEjOKL5OvPOiUcpMC2gdKCSa4BxHXuOQx8Exa/J7aaowhIwUubroDhsIK/ToDr5Cp37t8ArhAeZ7ejna6YCuBN/HBb+FFjMwdY/SRPeWJxhhV5eKSw7vJKRYPOE+pJ5uKhRGjjem5lvY0e/gsgopPQ4/1z3yLldXG7i8Sj+pe+/J9iepddRS5Bnbjy2LyOhvPITXMdyXCTjcNJvd1NfXx5271NdHpVTNsRFtKIhoCTTsxhYpJxSJYnEHsHW24OxKX4EVjkTJkICtr4v6eid2f5h+19j3x36zlxwVFMj87DvWzjlZopRZEvIRlY5PeiutR6m0HKN3/2YsgeTv4pPVF49EIpM+zge2DyiSFSXdvtJ8GHPpdZTIDbQd3MmANrk6zdCyD1lUgzPQzsef/Tg3lt7IEsOSuHVeaXqFy3KvwHS8keN732JAO7F63+P00GZpS9jGVK/xXuteNBEN9fX15JHHtqPbpjQOONVI997a1raN7MzsCbcJ28Mccxyb8n3b5e8iS5aFz+zD7DJz8PBBZBnT41H8YRvjvnb8NRZrzsUiCXCgyUx9Sbww4libBaMqc8yzdKYwledJIrjsQcwWF/X19WxtcvFivYO7zs+jqctKRnEHm49uZnFk4vffVGCQRdl1qAFfwfjP9kTXt9M5QJYyg6NHpzZZNVeRRZdfjXsS1zZXncnbe+ooN4oJiNKIHGtDLR6nGoc/TJM1wPIiNU2d/cxWB6iv98VtLwkHmOd30tDWB5KxApfpuq8OOw/j9Dip0dewo34H4azhe3muPsIT249itvmQODqpd02/97svGKbXFWBhnpKjR49SpvDyxAEbl5UkV7vP9G9KQxE5Rx7DEsmlQJ1P9N3f0KRfF8sOkbnaqXzv10Skapove5KMkI+KLbfgmnU1iqNbaSu8JravOW178HqDmI0Xk7//JQY0VVin0PaZPvekxHMwGIwjnUGojicLrVaLxzM8Mx6JRJBKpQmXeTwedDpd0m02btyIXi8UGhs3buQHP/jBhMdXKETnt7q6Oo1WV0Pu45Q9fxtEbxRp6BUXwJu/g08/BYFeNEsvFJ6GacLSacHf4p+4Pa2r0CrckFa7x6K+vj7Nc/9wYLzzDrWEWDRnEc2SZvQmPdVVU7g+vUfBWEb1goXDn8m7oO8djNXVwux91+/hljfi/DYlPRLml8+nulIc23TYRF5FHkUpdLwngm2Pl6yypeQOnb+xBOP8C0V7EsEQgCOhM/4+2bm/jmVpnMNkHrCTe56kj95n21m3fAG54yQn31rs55KH3mHOvCpkmdNcLp0EI39XW/dvZVX5qtjf0WiU6P4ofVEl6xYa6fK0oS+ooCxbzX89fx8/XvvfLM4VHcoXWuq5ekU5CxZUsePQDsq0ZXHXNLchlwPeA9yy6BaqZ4nPy7rKaPa0kCnPxasuYGGRfszseO3uNlbNSvz9bCqbzX0vHGJppZEvbKzCoJJReLwQQ7GB6hyxfnNzM0cGtmILnINE8zaBrA1UV1THzrvF0UJ0fxRb2JbyPVBdDX/c9xay7NK4IK+GbhcWbwueTF1sX4GmAEvnLqU2WIs2Txt7PpxK1NfXs6fHzepZ2exotrCiejbVs8Ugfb6nnZ3NlqTXYov5GDeep+YzGyZWV0WjUSJPt1I5Z960qh4mi3Tfnafz8yQdXBTo5IUjB/hUWQHV1cIDVLbTgbzNzbHMSioVQaqzgfzBNv/LjL5mPcUFSc6hZgfJvv2R13i+u40PTqT+u0obBj/s/gEFi9YJ1fPDD6GvmjccFmhtAV0h1QtndtALie+tnc0W2qL56CM28lO5Bm1OMJWnfL3cL3Zx7uIq/ivTwPr5eYm9ulPEiZYTtPpaKZ1dilY+aj+1RWjyDHF9V4/KyuZmcc5x594rAY0p/hwsNeDpo6C6GrPdh0ljZtHCBZNua47WTPWcSqpnZROJRIn+o5OCstlkjVAN7nW0sqBUxpI5Obxa1zXcnj9+BC79MZSN4+395t/ANJu8UCd543wXJ+N5ApCRkTHp39C22m0sL12eePtIGP7eyayVG+Hwr5lTYIDScY7T9wrIFzDHG6JAU8DB0EGur74+bpW2o23ctfguZO1vM0frS2m8o3AoeNz8eMI2pnqN99XvY1bmLKqrq7lAcQFb2racVs/gdJHOveUd8PL+wfd57LLHmGWYNe66ri4XO2p3TPnaOLuclPSUsHjhYoobi1EVqZiblb7NQCJ8mMa4/pAfyx4LXz3nq/y9/g18nYox5yY5VMv8ShPV1aUn5ZkyledJIhR6gzhf76a6uppH62opzTXyna292LxB5pR5qHd3MmfeHGSZMxeeWLnfh8KYT3X1+LYOia6vo9lCWY5n6tek9dOUnvtx0I3NPJkIi/Z4Capzqa4e5AsOl6LN0UF1Nc/uaefXuxrYcc9yul/pZvXieVQXjapssjSBviietxiB6fpNHWo4xIrwCtRSNWji+7mf0Nn5+MPvcdG83Cm93yeC/oVOairzqK6upnJOmB9ve5PSWXNjVVCjMePPkzmVsOt7qN0H4dxb4dibVEs7oOoysfyF/4Xzv0qmq5uqnXfDgA/O/wqmFZ+HhxZSPW+OqDD0WsHbg8HTjKG6GrbWwfo7yS+afNtn+nmSlKWoqqri3nvvZfPmzWzfvp3XXnuNe++9l6qq5CEE42H58uW8844oQTtw4ADz5g2XcM2ePZvW1lbsdjvBYJA9e/awbNmypNvccsstHDx4EIAdO3awcGHiH820oHKt8O3troM7dsINT8AFX4cDT4jyxgTeOKnAHXTjDDon9vzMqxYhhqPSwc9icgiGg3S4Ojjcf5h2VzuzDLPQyaYhXDCR37duMNAvEoFt/wP9DXBie9wqjoAjzl9tOn2eMwZcw2E9IB5oleuSb6A88z2ej/W4eGBL96luxrTA4RsgEIqQox2/nC5fr6QiR8Ou5lMTMnjUepRq0/BLSiKRoJfrOWjuYlGxnvJsDU39bu5/4RDNth52tZ0ABMG4+VAXly8SJeKuoAu9PL5jNMc4h0ZbY1w5aJ46D7VUi9ke4POP7uaDBOGKR7qcLEwSCFhgUPLo51fxg6sXYVCJTm2+Op9e73B6eJenC3/Yi8z4AQNRf9wygIN9B7mg+AJsARuBcOpq5KWlxjG2KO809lGgV3K8100kGqHB2kCvt5ccVQ7ZquwplxNPF7pdA3TZ/fz0WkHIjQwXzJnAasPs8FOUYgiLRCLBqE7swXoWJw9LBn3N80d8b1qVAr8il/dYxkD5Oqh7DhrfEHYPthOQnX7J6GiYNIqZCxcEyKkSFgG588VgT5s/7LUHyf2dTxLqOh20RfPItLdOvDKAswMMqfVDo9Eo/e4AOTo5t66dNSXS2TvgpcHWwALTAmr7aseukI7VRsAZ31cBIeiwt0Hj6yhf+qKw9vnDxel5X4/AJQvzKRu0rcrIkLCk1MCBUc/iNquX8myNCJ4aWYJtOS7KYpMhGoW6F2DD98B8YFLtO53QaGtMbrNhbwVNHsjVoMkVPs/jwdUNukL+/rG/88B5D7CtYxsD4eFnuyPgoN/XL8jP/EXQk5p9VY5q6h7PzoAzFp5YnV1NvaWef7X9i2cbn41br83Zxt6evVM61umGZxufZUX+iglJZ4B8TT49nsQ2Yemg29NNgUYQbENZIGcxFl2eLgo0BZgUJoJRd8IQPqsnQPYZbLVhUMnwDQiP5UOdDu7aMI+939lA7QMbsfr7KdQUcqBvZp+lhVMIGHzveH9c8PukcdlPJkU6AywuMVDXKd6xoXCEZo8iZrVxvNdNjzPAX3acQAJUF+rG7sDZOWkOKx10uDoo1ZVSoiuh3dUet2xhkQGdQsq5s2a20qQsW828PNHfUcoyqS7Un1qfZ5lSWL3VPimsMeZsgPadYlkoILJRzr0dLv8ZXHQPXHAXnP81UBkF19Q12B/pqRPZI84uYffq7oaCJUkPezogKfH84IMPsn79eg4ePMjrr7/OgQMHuPjii3nwwQcndaCNGzcil8u54YYb+MlPfsK9997Lyy+/zNNPP41MJuOee+7hlltu4YYbbuDaa68lPz8/4TZDbfvxj3/MzTffzL59+7jjjjsm1aZUsK9nH67z74SbnhE+uQDzLhMKVkPJsEomTbgH3IQiIXwh3/grFiyGg8/AjwrSSzk/i4T4Z/M/ufKFK7nzX3fyrZXfwqAyVybnAAAgAElEQVQwoJNPB/HcMpZ41heJh8HRl0GuhY3/De//Jm4VR9ARCxeE6fV5zgy6QDkiFG3DgyJYKRkUhjOWeH6roZcOm5cOmw+98uSofmcabRYxCE4UwjYalyzI580jp4Zwr7fUU50dPzsqz1BzrL+fVRUmKnI0/HrrMerMDjKlPpqsYgBzvNdNNDrcIXIFXehk8Z2jOcY5qKQqyvXlsc/y1fkY5Eb2tNpwBUKY7WOfoU19bubkpU6q5Kpz48hls9uMWqomt+Rd5BnyMYOug30HWZa3jAJNAWZ36qVhNaVGDnTE/763Nfbx2TXlHO9zs793Pze/ejN6hZ4cVQ65qly6PeJ7PdVezx90erl4fh7l2Roe+/yqGIkDw2Rhj9OfMKyly+GLCyOcCEaVDLtvBsnHs5gQ5SY1OqWUghGhNDqllB7jUv45sBJJ1SWw42F45rPQvE0MYGRTCNoZhEkjn1niWa6GL78POuHtOsbnOZm/80nCYbMTh6IIpbt94pUBHKkPHt2BEJkZEtTyqfv/1fbVMt80nzXFaxKTcgmI5xytIJ7HCC78TjHxPRLGcjEJcGI7htY3WKboBPO+SSfB//DqxRQZh59By0qz2N8WP8l/ot9DebaafJ2SHucgQe53gN8ugr6ToWOPyM6Yf5XoQ7knIGNPcxyzHUtOPPcfg5xBlaomJ0XiWRAr+Zp8yvXl7O7eHVtc11/HguwFZGZkijDSFIlnnUzHQHggLqwqXdgDdgxyMeFRri/H6rfyg50/4O32t+PW29K2hcfqHkuwh9MDB3oP8Gjdo+OuY/VbY9cqEo3wRP0T3LL4lpT2n6fOo9fbm3I4cjL0eHsoUA8Sz8ZZtDjO+jwnQpe7i0JtIUalEW/IQX+CED6rJxhXrXGmQSKRUGZSs6/NRqvFS1WBDolEQjTDhyJTwfqy9bzb+e6MtqHAoJqUx/OeE1b+trudb106OSHmdGFRsYFDHeId+9QH7bx8zE93txiPHOt1M79Ax09fPcrVy4oTjyOd5pQnraeCdlc7JboSSnWlY4jnzAwJd66fy2ULJ0e+p4rrV5aydl5u7O/l5VnsbZ0ekV+6aLd68QRCUHW5sKIrXgGFNcOT1l21ImBalSVyL6ouh6WfHq6WLz0X2gZzcLoOQtEykef1jztg6WcmzUueLCRlaCQSCRs3buSee+7hhz/8Iffccw+XXHJJSiRIwgNlZPDf//3fPPXUUzz99NPMnj2bq666iuuvFyVX69ev5+9//zvPP/88N910U9JtABYuXMhTTz3Fpk2beOihh+J8oKcbv9jzC943jwo6yp0P2tyxRGMacAdFmJ0zOAHRV7gU7qoTymdbii/pV74Bja9Pum0fZtT21XLr4lv5yrKvcOWsKwGmiXg+MfZ+UOrFg+K1e2H9/bDkBjFAHxh+0dkD9njiWWmcNsVzZtAlZsdShVIPAdcZmcr+qzcb2dbYR4/Tj2kqYQ+nEVqtnrhgwfGwoTqftxtP/mDXEXDgDXkp0gxbw7RaPNjdUj59Xg46pYzKHDX72+x869JZRAnR4RTtfL/JwvlzsmPvFFfQhU4+injOmsPcrLliUDqIPHUexsEJlfkFOjrtfk70e/jzu8PPx6ZeD7NzNSmfx9DAaghdni4urbgUV8jKutJ1ccv6ff1sbdvK6qLVFGuL6XSnHkJVM6h4/ufBLl6uNeMJhNjfZuPTq8rocvhpsbfxkbKP8MLHX0CeKWdVwSreM79Hv6+fS567ZOL3xQyixRZkSYkYpF88P4+MET7V2Ro5FneAzz26m59sHlt21e3wU2hMnZTMUsuxec4qnk8lMjIk3H7hLBaNqBzQKWW8PPeH7B8oRbngSrinDRZ+At77legXTQOyZ5p4Bsgb0dbRxHOiSeSTiLpOB/OrqlEGrUL5MhHSUC1Z3EGyJ6igSRV7evawMn8lK/JWpEw8axRSpBkS3IFRFXx+OyhGE8+lQvFsPkAECTc5/iQ+d8dXn0wWy8qM7G+3E45ECYUjALRavJRnq8nTC4I8EomKiQgQdmrJcPh5WHQNZGSIPnvXmat69oV8dHu6KTeUJ16hvxFyBknplBTPXXHBl+tK1rGza2fs70P9h1icM2hrU7BYVJemAIlEIsJ3/ZOfkHUEhysOMyQZzDfNRy/Xj5lM7vH0cMyWmof6qcCenj280yGeYS83vYw/NJZM+/W+X7PpiAiwqu2rRSPTsDA7tWphlVSFSqaa8thkpOK50lB5VvGcBJ2eToq1xRgVRtwDThFkF4rErWPxBM9oxTPAx5cW88NX6pmXr0UuFXRUr7eXXHUul1ZcyivNrxCKzFy1d4FeSZdjAvFfAvxlRytf3zCXfP3UJ9qngt833kVd71Gc/gF+teUYpUVFHG8VgbzHel3cuX4ugVCEa5YlCA8MekQQ3QwqnoPhINFolHZXO6W60oTEM8BtF86iIif18dpkcPPqCmaPqPBaXpbFvrZTQzzf/Vwtrxw0w/wrhJpZroGiwX5DNCpCBEsTB38DsPBqeOcX8OJ/QtsOoXAuWi4Uz2u+cvJOZJJISjy3tLQk/ffvhE5355gyayQSoXo2VU56v64BQXROSHhKJEI5Oyrhe1w0vz3G0uFDhUhYKGQmgUP9h7io9CKumXtNjPDSyrWx72PSsJ1IfD/oCqFstSijkCmFSn6EYscRiFc8m5QmbP74h+Fhs4O7n01QyjoBxlhtTIRMGUiVgnwG6DkCf7kq7eOebAyEI9R3u+i0+ehxBshWn96zfaliaBCcCmblauiy+xkIRyZeeRoxNJAY+i29fribjz/8HhqplvPmiLavKDdxzfJi5haK102PW5TH7miysHr2cHmVc8A5hnjeULaBH1/w47jPluQu4dKKDXx0cQGfOa+cTpuP95r6+cM7TUSjUdyBEA7fAEVpKGzHEM/uLq6afRWKTAVXVF5Bj3dY8fyjnT/iE3M/wWzjbEE8u1InnhcU6mnqc3Pv8wf53dtNbD/Wx/LyLLI0ckqzVBzpa6VQMzxIX5K7BGfQyUN7HyIYCdLvPXW2G622IPPyE5TrAdlaOWaHH99AmJcPdtHU545bbrb70vo+jGoZdu9ZxfOpxlfWxw+udEopZrsPrUJKRmaGUHlWXSb6G7nTo/4xaU8C8TwShUvi1aw9RyBv5rwGx4M3GKLD5mP1nDys0tzx+3xBLzzzOTj4tOhjpIB+d4Ac7dSDiwFana3MzZpLTV4N9dZ6guFR31kC4hkgJ5HdRiKrDU2uOMfOvezPv4557t1iQmDaiOcsDrTb+cQj7/H4+yeIRqNisjdbg1KWiUaRidUbFN9B4VLoS+JjGInA4Rdg4WDYT+GS8W05TnM025sp15fHh7517BU+139cL6o94xTPE7yTRiieAQo1hfT5hsnqg30Hh4ln02xBVAdTUzHnqHKmVAk02ururhV38dBFD9Hp7oxT9/Z6ezF7zDHB0OmGNmdbrDLq5x/8nCZH05h1ujxdMeuCzc2buazisrSOka+eut1Gt6ebfLWoNCnSFE2LfceHEV3uLgo1haICNmAnSy3H4ol/Zto8QUxnOPF87YoS6rudLC4Z/g32efvIVedSZaqiSFPEtvZtM3b8AoOSbmf6wd3He90xEcapQjgSpsFWj0pr5at/28/6+blsXFmNw9qL2e6jzxXg0oX5vHLnBZSNHkdGo/DYR8WzdtlnZqyNt71xG9s7t9PmaqNUV0qxtphuT/eMTiakiuXlRva32cXk8klEIBRmf5udDptPKJovvFss0BVApkL0N9p3CfuNZKi8EL5WK9TSR18Rauf5V8C6/xJ84WmOpMTzfffdx80338z3vvc9Hnjggdi/733veyezfacU3gEvVr91LPEMcNG9sO7bk953yornIRgnIJ6jUehrhIBbeOB1pU9UnjE49Bz8epnoDKcB74CXdlc78wxzxD4GUagppN2ZYllrMliTqKQu/bHw6BlC9uyYR2EgHCAUCaEakZZuVBjHEM9Hu1y835R+53qM1UYqGFk6aW0WSjDL2E7s6YTGHhfBUASz3Uf3h0nxbBGD4FQgy8wgV6eYVNnYVNDr7SVPnRezV9h+rI+vXDyHFaVFeAbEM25FeRa//NRSHAFBQtgDViKRKDtbLKyelRPbVyLFs1KqjLPZAKGU+Y/FX+CRm1ZQka3BbPfF/MzMDj/NfW4qczRxityJkKfOiw2Go9EoZo+Z+ab5bLluC3Oz5sbeAbagjd3du/lyzZcBKNGV0OHuSPk4Slkm8/J1XL+qlF6Xnz+/28KGajEQm5uno9nWTrF2WIGQIclgY/lGXmp6CYPCcMr8nqPRKK32gaTEs1ouRSnL4Na1s/jPi+fwhcc+4K0Gcc08gRDBcASjOvWQmCy1HNskPZ7bLJMvvT6L8aFTyui0+9ApR3yXs9dDpnzaFM86hZRAKEwgFKbX5efZPVN8N08EfQm4eyA0SJz2HhYl/6cAW+p7qSk1UGxU0SXJT24rEQ7BMzeLieK7DkPJipT23+30p00837n1zoQEkcVnIVuZjUamodJQSV3/KKVqEuJZp5Ti8o9WPCew2pBIhOpZk8s76o2EMhSw+FPgmR7i2aSRk6sVFkHHe930ugJoFbJY2FC+ftDn2d4GJauE4CGRhUbHB2IAOaSi1xdPGzl+KtDt7aZQOzz5idcKz34OVnwOLr4PtHnDEx0TKZ4jEXEttPmxj0xKU4wsDkfCHOg7wLK8ZWJhplT4R6f4HeeochKPz1KEM+CMy5VYmreUWcZZyDJkcereHm8PsgwZx+3HJ32smUSbq40ebw/esBdbwJbQrq/X28vBvoMMRAZ4o/UNLqucBPHsnSLx7B1WPJtUJqz+s/aRidDp7qRIW4RKqiJKFJNWQr9reGLPPxBmIBxNGox2pqDYqGLdvFyWl2XFPuv19ZKnygPgU1Wf4pnGZ2bs+MLLPz3FczgSpaXfHaeePRXodHcSjATJN/mo73Jy/0cXoMvKZ7Y2yPdeOkxFtgZpZkZcxVoM3YfEc/3GZ4cnEacZgXCAg/0HeabhGRSZCvRyPfJMOTmqHLo8XTNyzHSQp1OiU0pp7j85k4kD4QhHzE7qOh0EQhE6bQnuuyHVc9vO8YlnENXsH/sN3PKmmBivXCuI5zMASYnnRx99lPz8fH7+85+zadOm2L+//vWvJ7N9pxRDhELCl63aJDpgk4R7kJRxpuqpOxS0kgw9dfCnDYJwNpSK/07FMsHRKRQOp6PtQvdBoSp56tNpbXbEcoS5xrnIfTZ44fbYuS3KWUSTownPgGdy7YmEReCKMUFp4rxLQDPCNN80K0Y82/12DApDnH1NliILeyC+09jt9GN2+AiExnqnJkU0SuZAmlYbIDr9Q4OmoQHF4efT28dJRl2ng2KjCrPdT6/TT86HRPF8Ig3FM0Bxloqn6p+bkudhuujz9WFS5HLB/7zF+8f7+aDFxqoKE3qFfsykmjPoRIIET9jBkS4nJo1cBEYNLR81CEwFRUYlnXYfTX0edAope1ttNPW5mZ2GvzNArmrY49kZdCKVSNHJdRiVxjh/w3pXPcvzliPPFEqTdK02AP742ZX812XzuXRhAR+csLFhgRiUz8nTYnabKdLGz1hfUXkFK/JXcF7heaeMeO52+pFnSsZV2Hxj4zyuXV7MLRdU8uDHFnDX0weIRqMxf+d0bLqMGhm2SSiem/rcXPHr7SddxfDvAr1SSqfdh141gnhW6GDNV6E8NdXtRJBIJGSpher5H/s7+dP2Ga6yy5QKlYijXUzcu3vFe/okIxKJ8shbx7n9wtnk6ZWciOQlJ547dgt/xo8/LMo0U8CvtjRy7/OHuLgq9X6r2W3m7Y63x9rNARa/BZPSBMDyvOXs690Xv0IS4lkjlwp/w5HwO0TGxGgYSqF4OXUDhbx/1TZRVTaN/snPfGk1P7h6Ee02b8zfeQj5eiW9Lr/o22WVC7u7RKpn8754xbkm94wmni0+CznKEX3Wd38J8y4VIetzNsBNzw5nhUzk8eztF/eBdPi9ka3KjtljNNoaYyG6MWiywZOa0KJYl/77dyRGZ6zE9qstjrPb6PH0sDx/+WlLPLc724lGoxx3i/YlssQYGsc+Xvc4ZbqyMRP6E2E6AgZHWm1kK7PPEs9J0OXpokhThEQiwaAwYNQNxPk8C39n2aStT08n/OHmlVy9dFhsMaR4BthQvoH9vftnbEyTpxMTj6E0KkXNdh9ZajmaU0z6N9mFIGxOUZhHblqBQS0DlYkKVYAt9T3MTSISAUSV1JJPCWuoGcIRyxFMChPvdLxDqW44M2O2cTYfdH8wY8dNBzUlRg6bZ96+MBqNcu/zh7j64fd4fl8nVfk6oXgejcKl8Pr9grcxJLBHSYTSc4a9n88QJL3rVCoV3//+9zGbUw9O+rCh09WJRqaZkXIgd9CNSqpKXfFsKB32mksETz8EHLD79zBrnQiz69wH7/92cuRxz2Ho3AO9R9LfdqbRWw/n3C48inypB/Ed6j/E4tzF4LNBJCT+CygyFSzIXsD+3v2Tao75tW/xldIKUKRAdplmgVW8MEb7O4PweB5NPHc5fESj0G5NY2Z2wEtUkilKodOBdoTaxNMHZWtEWvtpjEOdDi5ZmE/nkOJZfWarAIbQli7xbFTwZNOveaX5lRlsVTx6vb2YLTJCkQh/ereFDpuXhUX6hL7pjoCDIm0RmTIP/7v1WCxMYkidk0jxPBGKjCIc5FiPi48tLWJfqy1tf2eIV/OY3eY4xZdSqkQpVWIP2DnqPsry/OWxZYWawliJa8rH0iuRZWZwzfIS1s7NoXgw8Ko4S4V9oGcM8bw4dzGPXfoYuarcU0Y8N3S7KM8av6zzixfOjoWWrZ+fj1KaSYfNh9nup9CQnhdello+KauN5j4PrkCIE5ZJTiKexbgYstrQK0c9Yz/yXTE5Pk0YChjcUt876dT5tJBVIaqWeuuFAugUhLO83dhLZoaEi6pyydMrOBbMTk48d+6FirWCNB8HDu8A//VcLb5gmMfeO8FrX7+QG89N/Xva3b0bZaaSXd27xiyz+CwxwnBFfgKf5yHiecAH4eHqBa1COtbjOZHVBojvovRcepwBTHlFgxPj09cfz9EqKDepabf6xlhbFeiVdDsCQvBhLBOK/t4ExHP3IeFNPIhjHiVu66kJ+p0O9Jv3ktP4pvgjGoX6VwTpnAia3PGtNlxdw6HsgzApTVh9gnDc27OXlfkr47dR5wjCOgWUaEvocKVecTQao602hjByQnkgMoA1YGV14erT0ufZO+DFEXQwN2su9S5xfw5Vl41cJxAKsKZoDY/UPsJnFyb5PsfBVBXPzqCTUCQUG/Po5Dq8A14GwmezHEZjSPEMQoykUwfoG0U8L1b2CyvEM/z6yaUZcdWJQ1WUILzFq03VMYuY6YYsM4MstTzu2k6E473pBZdPF/Z074nr/zc5moTNodTOivJBxbjKiHLAzkcXF7KgMImIJxKGQ89CzQ0z2t7a3lrWl61ntnE2JbphEvU/l/4nv9n/mzHPqFOBihw1J/qnZ1KjoduVVPDy4gEz9V1OvnBBBU/sauMTy4vptCfgc5Z9BjY8CLf9a1radLpi3OmORYsWsWzZspPVltMOne5OanJrplTKlQzuATfF2uI0rDYmUDwPduQ48qIwGi+sEeVxb/1IKBbSheU4IIGGzelvOwpb27Zywys3JDSVnxR664X6JKtCqFFSQCQa4aWml1hbvDZGOI9UaqzMX8me7j1pNyXa/A4PmLewDW/CQI8xMA1bbSTq9I4sQxxCt8OPNENCazpkis9OWJYeiQfEq3U8fVB9pRjojTfpcZLxk831fGnTXhp7BLF5qMPBxgX59Lr8mO2+D4XHs38gjNUbpDAdX1y9h3B0gOcan5ty+niq6Hb3cuBElD99diXvNPaxtMyINDMDvVyfkHiuMFSQKXWzraGPz6+poMHawCV/v4RDfYcmRTwrZZnoVVIsniAfqyliR5OFfW22tMvgDAoDoUgIV9CF2WOO81mGYQ/oo66jw2XBQJYya4w1TqpYUZ7FX//jnOFj6GT4I7YxxwahAs1WZZ8y4vlYj5tyY+pWGQCLSwzUdthjiud0kKWWYZ+E1UbLYNlc3UlQMfw7QqeU4R+IxFttzADm5ut4+oN2jpid+AbCMSufGUNWpQgV7D0MeafGZuPNI71cs7wEiUSCTiGlLZpPqD+JzVXnXpGEPgFarR6e2dPB3c/VsrBIH5vkShW7u3bz6epPs6trV9w7ZSA8gHfAG+u/LMtbRm1vLeHIiO9piHh+5Ruw9/HYxxqFFG9w1PeZyGoD4JIfwjlfpM/lJ0+vEKHenj6hTO+fHhKwOEtMXjb1u6kYsrbqP8YcpV1MethbRf+7YJEgmUej+6Docw/ipWNB3NZTX0o8WfT3HyHHbhY2GX0NgtgacX5xmMhqwxkfLAjinekIOAhHwuzt2cuK/FH38UQq6hEo0ZVMWvEciUaS9jmKtEUxxbPFZyFLkUW1qfq0VDy3u9op0ZZQqCmMEc+j+yRDgW1L85aSr85nfen6tI8zVeL5UN8hFmYvjKl0MyQZ0xqm/mHBQHgAm98WI1+NCiNqlX+M4nltxkFhhTji2fphQJ+vj1xVbuzvlQWTG5unCmG3kfrk9vHeU2Oz8f0d3+eqF67i+zu+z96evTTbmzm/6Px44YvaBD47v7p+KbeuHZE5VfuUeBaDmMzOlM+YxUbskH211OTVcFnFZVSbqmOfL8pZxLqSdTx59MkZPX4qqMjWTItIJRyJ8rHfvssdT+xL2Ff94ISV61aU8LWPzOWcShNXLy2m1+Ufq7Q3lsLi69LL5joDkZR4fuCBB2hsbEy4rL6+ngceeGDGGnW6oNPdybK8ZfT5+qadyHEFXRRqCtMgngcTvpO1w2sV6lQQ6ovCGkACt78D7/8WmSdN5brlOFR9FBpeE383vAaH/5HSptFoNE4ZcMRyhChRPv/q5xN6j6UFn12oYwylwtrClhrx/FbbWygyFawpWpOQeF5VsIo9Pem/3HYcfQar2kC2MnuMUjkhTJUx4tkWsMVKVYcwOuQMoMvhZ3GJgdZ0vEv9DiJpknjAoOJ58Lp4+oQ3X9l5wnPoNMGeVhtRonzvxcPYPEGa+zwsLTWSrVHg8A1gVJ75xHOb1UtJlorMNHyKpcpeTBkLsPgcbG/bN/EG04A2ZxfazCxWVphYPz+PcyuFAk4nS6B4Djqo0FcQlnj4+NICDBoJX3vraxgVRhptjUSJoshMP/iqyKhiVo6GmlIjOqWUzAzJsAIgRUgkklji8lCwy0jkq/NpsjfR7e+OS4LPUo61xkn3uEOQKlxIopqYjcdonFLFc4+LcmN6QTZLig0c6nBgtvspMqaneDaoJufx3NLvodCgpK7z1KspPozQDSqd9TPso//dK6r558EuVs/OHlSezrDqOatCDMh6jgzbCEwCA+HIpEnync0WVs8Sz0+JRIJHU0LYekIsPPgs/H8XiH4eDBLPyxPvaARs3gEMKhmvHOziE8vSS6+PRqPs6t7FNXOuQZ4hp8UxbHli8VvIUmaRIRFDiGxVNnnqPI5YRlTIKY2CeG59Ny5sT5NI8ex3gCIB8ZwpYyAqwe4dIFujEP0Rdy8cegZeuSut80kGhTQTk0bOrmarUDxHIvDM5zjf8jw9Qx7PxnJRBjs6OyUUhP7jQggxiEMOBarg5APvTjX6XZ3kDAwS7g2boery5KW86mwheomE4YM/w+a745e7uuKCBQFkGTK0ci32gJ19vfuSEM8pKp7TzFiIoelfuPwO1FI10ihjlOxF2qIYod3j7SFfnU+Zvmz6xDMp4MXjL3Kgd6zS0+a30WBtiP3d7mqnVF9KgaaAY+5jlGhLxvRJer295Kvz+cScT/DIhkfInERFx8hrMhns790fN2kPg+r3f2O7jWg0OqaiutvTTa4qF2mGeMcalUbkcl+cx7PVE2RR+Aicdwds+9npaYk5SfR5+2KkO4ix+e7u3TN2vHT7F019p0bxbPFZ+POlf6ZSX8nX3/o6+3r3cX7x+fF+yQoDDHiQEUGWOYLe2/IgvDr4bO6pm/EMi2g0Kojn3Bpur7mdLyz6QtzyC4oviO8rnCJU5EwP8dxp82FUC3vAl2vHcm31XU6qC/Wo5VKeuX01BQYlOVoFXYP33b42GwNp2L2c6UhKPH/jG9/gySef5LrrruPuu+/mf/7nf7j//vu59tprefrpp/n6179+Mtt5StDh7hCewJnyaS8LcA+4KdIWjSFnkkJpgEwZvPVjeO2+scu9Vqg4H1b+h/A/PueL8Nl/iFmtuZeg6dqRXgMtx0V5nbUZfnsOvH4v/PObwr5jArS52rhp800MRARx0O5s5zPVn2FD+QYe2vdQeu0Yjb6jkFslvImGBosp4K9H/sqti28VRM8Q8TzCh29xzmKOWo+mPcFwoq+OZaYFZKtSJJ6NZSLlOxTA6reOIZ5zVbljJjrMbjO5hQfTUzz77YQnQzyPVDy7+8Tf5WugLc37ZwbR4/TzzUuqOGx28JcdJ7iwKhe1XEqRUUmuTpEWWXu6otXipdw0vs1Gh6sjrtMezOgiGizA55jD32rfm+kmAtDj6SNfIzqJv7x+KV9aNxsgqdVGjioHrVzDNy8vY1/PPnJVuVw560rqrfXo5LpJedYVG1XMztOilGXy3JfXsOmWcylKU9kHUK4vp83VRquzNc6TDIS/4SO1j7BAvwBZ5rDaUyfT4Q/5CYbTt4UYjUiGlUgwuSd7jirnlBHP7VYvRbr0VK5LSo0c7HDQ1OeepOJ5clYbVy4pPEs8zxCGlM76GVY85+mVPP6Fc/j6hrkU6JWxDvqMYagvcWI7FE1M6CbDH95p5hevN0y84ih0O/zYvUHmFwy/s0P6cqSOVhGi/OZ3hWVFT514L/sconpqAtg8QS6cl8t3rqjmo4vHVlKMh4P9B5FlyCjXl3Nu4fgnzDkAACAASURBVLnsGNGHtPgt8b68wEWlF/Gv9hElokqDIB7tbdB7NPaxVpE57PEcicCRl4a9gBOgzxUgWysX73V1jiA6O/Ymtr0YQvPbEEq9fLrUpOJgh12E+da/BJbj5AbbcNl6BamjyoK8BUJlHRrxXOpvFH06uXhXR6NRDvRnoI54CAaD1HU6+N3bTZN6lp0SeK1Ywj6y82qEzd4Q8ZwMmTLhj25tEerL3X+E5m3Dy20tCS14TEoTx+3HCUfD5Kvz4xemYbVRrC2my90Vr7SfCPZ22PQJnC1voVfo4Y374fcXxoVoj7Ta6PH0kK/Jx6Q0TWmSOV282vIq73a+O+bzxw4/xm/3/zb2d5urjTJdGQWaAgaiAyzMWTimnUPkuVqmZpZhcv71QxPzk8WB3gMszVsa99lI25V/R+zp2cOXtnwp7rMOdwfFuuFJQqPCiFTqi7ODaOx2Ms9fB+fcBp97+YzzeB0Pvb7emMczQE1uDY22RtzBmQmBKzCk1784FYrnQDiAP+yn2lTNZxd+luurrqfT3ck5BedgD9iHxx8ZGeI96htRReCzQ8AF3XVwfIuYXM+b/OR6KtjeuR2j0kiJNrFP8dysuaeFbVFFtoYT/VMnno/3uZiXr2Pt3ByO9cbfp5FIlIZuF9UF8RPrxUYVnXYf7VYv1/3ufW776x58oyvBPqRISjwbjUYefPBBHn/8ca6++moWLlzIlVdeyaZNm3jwwQcxmUzJNv3QoMMlXgDTkeY7Gu7goNVGquGCIFTPOx4Wao/DL8D/XQuOwdl+r0WoD658SITNqE2QPTgwmbMB7WiPvv3/N8Yb6o0TbxCKDA4ILE1CxXHHDrj2T/DlHfDRn8HLX52wmTa/DV/Ix1GLGGy0u9op1ZVy57I7eavtLbrcUyhD7D0yrC7JKh+22mh4Ff5xR9LNOlwdLMpZJP5IoHhWy9SopKr0Zt8jYbrcnRTlLSFLkbzkPhqNYnabRUlMpkyYxttOYPVbyVLGKzPVMjXyDHlMCe8fCBPQvkpT8BVODCqe3YHQxA8o3ySJ5zGK57xBxfPpQTxHo1F6nQHKTGo+Up3Pb/91nI/XCC+0IqOKfH16ysrTFa0WjxgEj4Nb37iVS5+7lNdOiKoEZ6iDfquRfmcmZtfJ6cxbA30U68TAUauQIpeKV4pOrhtTzeEIONDL9WQrTXhDDnaYd7CmeA0luhLqLfVpBwsOoTxbQ3XBJO71USjVldLmbKPR1sh80/y4ZfOy5lGpr+Srs+OffxKJJKEv+2TgCvURDmYlVUzmqHLo958a4tns8JGrSU/lurjYwAcnrBxot3PpwvyJNxiBLI18UuGCJywerqopoq7TcdLsZv6dEFM8j/Z4ngEsLjGwsMhAvkFJzxR8nh0BB8dsx4b7NomQVQHHt0I0IiZaJ4n3m/qxeNK/b3c093NuZXac16U+K5tQhhzqnoMFV0PFBcL6wLwPipelFAxk9QTJ1si5de2stMKQotEov97369hk/YUlF/JOxztEohGaHc3C31kZTzxvKN/AltYtwx/ItUAUSs4R7R78PWoUI8IFbS3CEq5zb9Ig5F5XgDzd4Hs9UyoG1k1bBTmZSBnrtcKT10NH6gFGpVlqIlGoyFaLPva6u9F721BY68UgXSLhT7u6caiK4wMGR/k797kDSDIy8WRoefvAUW78406e2NXKjqYzRAF9YjsWuYrsvEXQ9Bb0NQov8fFQWANdB0QuzEX3wJbvDS/rPpTQpiNblc2enj1U6ivHTjanoXhWZCowKo3p2SHWPQcyDY7jr2OIRAQZc/7XhLAmLO7LkeGCQ6StSqoiFAkRCKc4obHjYTFZNEm0u9rjqgxA2IO82vIqvb7h822wNlChr6BALsYSC7MXjqksHembO1nkq/Ox++34Qqmd00BkgGcanuG5xufocndRZ6mjJrcmbh2T0oQ18O9LPNdb6ml1tsZNnAyNl4dgVBiJZnrpd4n7zuoJsm33HlQyibCIyps/Zr9nKiLRCP2+/jirDZVUxQXFF/Bi04szcsyCNPsXnXYfpab0hS1TwVCewtCz8rYlt/Hd876LQWEgT5UXz0+psoatV0FMjubMgwvugn2bhJ3YDCqeo9Eojxx4hC/XfDmpkKhEW4LVb52xyYRUkaOVEwxFcEyiunIkRK6Qllrfn3m39/m4Za1WL0a1XIQ/jkBxlopOm4/H3z/B59dU4h8I82rdmWvRlQ4m7LlqtVrOP/98rrzySlavXo1anXrY1ZmMaDRKp7uTYm0xeeq86SeeB9wUatOw2gDRwdvwIFx8Hzz3H0LR8cZ3xDKfVRDPiTD7YtS9e4eVGk4zvPifcTYVwXCQb2//Ns2OZgh6RafeUCLK5AqXgEwJ868SHdHQ+AOrIRJmKGymzdVGmb4MrVxLqa50atey6+DwbN1IxXPnXqEGSoBoNIo1YB0O8vPZQKoc4yOXdlBY31HMchVFWbPHJZ9+sPMHfPLlT/KVrV8RH+iLwWnG5h9rtQHE3W8Hu5uR6uqxB7s51uvk/73RwOofb+W3b00wW+i3E5mUx3NevMezJhcKaoRKxHvqO4g27wAqeSZKWSZX1RSilmeyrkp0UoqzVMMD1DMcrRYvZRMonu0BO1+q+RLvdghVTJevlYAvlyJdFn2emVfmhCIhvCEnlVljlXSJFM/OoBODwiB8zP0W3je/z5qiNZRoS2i0NU6aeL5r41y+eOHE6r+JUK4vp9XZSoOtgSpTVdyym6pv4jcf+Q3qzLHfiVFhnLTP80h0ejpQZeTS60w8sM1WZY/xf08VyUIvUkE4EqXHESAnTeLZpJFz7fISHvv8KrK16VmoGCfh8ewJhLB7B1hUZECnlNHYc2o7tR9GaOVSJBLQq2ZW8TwSBXrFpAMGD/Ud4vK/X85nNn+G9zrHqQIxVULIB+d+adLqsWAowt5WG05f+oOYXc1WzpsV3xeozFZjlRcLf8bKtSLcrq9B2F6VrEppvzZvkCx1ehY5IJR4Pd4erp5zNQCri1ZzoPcALxx7gRteuYFeb+8YxfPC7IX4Qj6a7cJKjIwMYZ9RdZkIOXYKIk+ECw4SLfY2KD8fvvx+UhVWt8NPvn7E80MzODleuDRe9RwJC6/o/f8HIX9auRSlJjV6pRSjLCzI0pW3IHe2ketuIDrYrm2NfbRIZ4s+KAi1du3fxMT8IIaCp3xyE395Yzffn3OcyxfmYe7uFF7XL92ZcptOBaKtO+mXRMguWAr7N8Gc9SCd4P4prBH3pKNDELjWZnD1/P/snXd8W/W9/t+SbUnWsjW8986ehEAICRD2Hm1pGR2X7h+lpbSXtrSlBe4t95be9kJbOrgULtDLKBsChDBDIGTHSex4b1u2rL0lS/r98ZVky5a8MoC2z+vlF+TonKNzJJ1zvt/n83yeRxQahhqTiPk49Ao9u0y7qM5Nob5Vzp54hljA4GzsNsJjYr+NT8K5d+DoeY9cxyBc9jvYeCtIpHDvShhpTng8x60QClQFSCQSdHLd7OwCHQPw2o9iWTlzRzgSZtA9SLezO2n5nuE9hCPhBNHuDXnZ1r+NM7v3UrjnESBGPKew2jha4jlDmkGJpmRWYY6hSIjrNl/H1p6t7B7ezRUvXEGhsjBlps0/suK5xdZCKBJKskvod/UnEc952XlEpDZahl1sbx/lzpeauKF0gMyKU/6ulM4gRGvqLPUUu7kvLP4CjzQ9Mn3xeJ6YS0dVNBrF4g5inON49mgx6hvFqDAm/i3PkPOZhs8AUKgqTOYtdFVCoBeH+YgYOzRcIIqJQweOK/F8wHwA75iXTeWb0q6TIc2gJqfmI/fMl0gkVBpV9FiPTvXcYXZTk6/GHGynj6eTbETiNhuTUarL5s0jIzy9t58vn17F0pIczK7Zd2l9kjGzZOIfFIOeQTRZGjQyDQXKgmMaMBivmBsVxrkRz5f+FtZ+FVZ9EW7cDdc8KVoO+3YJxXN2GhW6ykhQUwF9MZ/eIy+L/05oZ2u3tzMWGRNVfmunuHlN9gHLlIm2uhkC/Wx+G5osDXuG94gQkWgYnVxU43UK3fw9vSJhoWyuO1f8e6LH80hz2omGJ+QhS5qFIjNGSvpsYKhLstoA0U4/J+K5byeDChVFqiJy5bkpz6vJ0sRbfW+x+crNmLwmsX+VEbyWlFYbAHnKvMTv7YWOl8iNnEKuPBdv2ILZFeCmTXW0mGKkij9NS7nfcRSK5xGhhg84RfU0IxOKVwhVy0eMYaefwpiq+cyGfLbcvBF5pvidLirSsrDo6JWvHxVGXH7ufkV0CTQOOFhUnJ6IDUfC+MZ8rCtexwHzAaLRKN3OLoqVFVy4uBpHwEn4KMhGIEkFEAqHEveqw5bDeMY8WP1WMlFRqpvadqaVaVMqnnPkORiyDbTaWhnyDLHYsJgSTQn+sH/OwYJxyDMzEkrro0GZpowdgzvQyDQpk+7TQa/QH5OAnA57B7rM0rQkm06uwxlwJiyMZgt/KMzaX7yBZQ7J3RNhdgXIUWYhy5j7JOc/PrWMuoK5f6+52TIcvtCcVMtdox4qDSqkUglXrSrh0R2z8///J2YPqVSCWpaZUD6fCBTM0+PZEXDw3Xe+y13r7+Li6osZnC7nQpEDa78By66e93EeHHAQCkdx+udOPI+4ApTqkota1XlqBiQFYrxSfqqwGDMfgZ7tgqydBgN2H57AmCCeVXMvEhwaPcSG0g0Jn1FVloplecv4xc5fACJ0cLLiWSKRsKl8E2/0vjG+MDsXyk4RqryON+DFb6OUTVA8O/pEXkfB4rQkSvy6TkCdL0jqouXi84ij9VX4VQO892uoPkPse5Yo0yupNKrEpDyvAZR6JOoCzsnchzOngWg0StOgk2aqxn2et/9ajJNWj/tXdsTasKPKPCp8TVzR/iMudD7FeXu+KYj37hNjgTVfuIcPkCnNRFm0QpD3DRfNvFHRCjj0tLD1y8qGqo3iu3bFyDTN1MK0QWHgoPlgatsHVd6srTYg5vM8CzKU3Q/CPfVChbz6X3BkZJAjyxGFg4wsuP4ZqDkDOt9BlaVCkanA6rdi8poSdiA5ipzZdTfFQ9ld8xPZmLwm1DI1vc5eItFx788t3Vv47ILPYg/YCUVCvNn3Jstz6zDufoiS0S5yMnMo05RhC9hwBBwcthwGYsSz6uiIZ5i93caQewib38Yfz/kjd59+N49d+Bg/XPvDKev9o3s8t1hbyJHn0OvsTSzrc/VRqhm3KGjQN9DjbuO/PrOcLzy4k2AoyGW+Z2HFNYQiIW55+5ZPdHdXOBKmw96B2WsWwYITbDbiWJ63HGO2MaX1zNGiMEcx68K20z+GPFOKIuvE5gjFFc+pMIV4brhgnOMBYXOV1xB7bi4QIYOG2uN2rC3WFlblr0rkP6RDvb6eVlvqHLkTiUqDiq552m08v3+Af9/cLIjnPBWjfhNh60b+3Pg/iXWah5wsSsFLLC/NZcDu4/ZLFlGcm41BLZ9Xt9wnEf8kntOgzdZGnU6kfhaoChJtV8cCrqALdZYarVw7N6uN+MBcKhU2GjKluMn074xZbaS3P3GWbYL9sRTRIy9BllKoRnrehz0PccQqBvAD7gFRpTekURAaapK80FLBHrBzeunp7B3ZS4+zh3JNeaLlQqcYt6QIhoPct+8+dplm2RLZu0MMSuNprLnlYnIRiYgJiM8Kwak3kCnKYp9N7GOSqqJQWYjJOxfFcwuDkjAl6pK0/m+/2/87vrH8G+TIczit+DS2DWyLKTrMWHyWtIpns1eosQddwxjlpZRry/njv1Ry91XLOKMhjw6zW3g3/bJOBD+OtiUHLfrshOejIFXnCw/JeCEjXnxQGT8WimeTM5Zuj5joFuaMK5wvW1HCLec2pNv0Y4+Htnfzh3c66B710GpysaIsvd+vO+RGlamiXlfPqG+U3cO70cg0vHrTeawsLUYmC9BnnUMY5STs6bFywX9vA+CI9QhXvXgVt227DYCb3riJbx34Fn9q/BPSSA4lKfyU44pnk8eUUAg5g05yZDkYs43cs+seLq6+mExpJgXKAjIlmfMmno8VyjXljPhGWKCbW+tirjz36ENTEcRzsaoybdtfhjSDXEVuSoVQOBJNW2j4oFMUrA4NzuFZMwEDdl/K7/h4QhYb3Lsmh5BNg85RD1VGQVBdd0oFz+8fOOoWun9iKrTZWcfd43ki5toK+4NtP+CDwQ/Y3LWZFXkrOKv8rKmTs1S44O6EV+98sLPLyppKHU7f3FVZ7sDYFCuM6jwV7SEjFC4RY7u8BaKry3QIyk6edn93vHiY5/cPYvOE5qV4NnlMUwJWN5VvYrFhMedWnMu2gW0pJ8KbyjextXeC3cbVj8VI84Xw6g9hz8OosyK4g7HPyN4nLOSmQVxFnIA6H4pXCsu1iYpnWzfUni06AhddLtTUs8T6WiPf2FgD/bvH1eTGWlZFmxiUV2Ny+rF4grwXrIO21wShuP1euPJPojAfQ4fZQ22+Go2xiH/Jb4XilSzr+CM9FArLOufAxzcILBpldLQZY7ZRtGZrS6Hu7Jm3K1ouxt5xZXPdOdD2+rgNSYqCgiHbQDASpCqnaur+VAbwzL6zp1RTOjvvYWsnnPNz+NYekEpxLL0KbcUkGxFDXeJ3E1c9dzu6qdRWAqL4O6si85GXxfjZPYf5xAT0ufqoza0lR56TpIY9NHqIkwtPRi/XY/FZeKnzJS5x2GD9dymwD/DrxXeL7ku/nefbn+fmt25OKKSneGnPA+Wa2QUsDnoGKdWUJuZ9Nbk1rC1aO2W9f2TiORQO0ePsYWPpRnpc43O3hNXGXy6EF75FQ5aOTnsnp9Xq2P3js/ndknYysnOh/nxe7XoVR8Axr2yUjwvu2HEH122+jl/t+ZUokGSnLpCsKVyT4CmOJYpyshm0z84+xuIWeQPzxVe2fGVePumj/lFxX06BUk1pcmdEw4Xi/hvvTDcfGbcnbbhAkNAZx2/81uXsSn1fn4R63ceDeK4wKOkend9ceWvzCH/Z3kXToJOiXAhGghRnns4u026i0SjRaJR320ZZWT417P7cxYU89/9O44qVoshkUMkYnaU4KBqNHhf1/4nCjMTz0NAQf/7zn/ntb3+b+PtHQJutjXpdPQAnF57MW31vHbOqojvoRi1Tp1QFzhn6ajGg8tqmJZ4dVRfDkc1CId2/R9ycPKNCPXPwbzRZmshX5gvi2dSYvhXDUDtj+5gtYKNOV0eBqoDHmh9LahuaOHBrt7fzt9a/8b13vsehUWGTEQgH2DmUJsH28LOw+PLxf8uUQqlk6xJtfrnlKVXP1oBVKK5H20S1z2cTA2tPsuK5UFU4J/9pv8eMOxLEkG1I227famvltBKhTor7JMY97NIpnid6ilv9NvKUesq15fS6xIC4wqBiwO4j2LsTNAXw7NfggU3w6JUiRABiVhvzCECQqYXPpa1bkPxxyLVCAf0RY8Tp/7vxcZ4Ib3CMx3f1sbwsl3vfbGNhkWbaqro7JO4hGdIMFhsXc9t7t3F1w9UoZZloZVqy5QFah2cOLnUHxui1jD90A2NhxsIR/rK9G6snSCgc4ZGmR1hmXEarrRVn0Ik75ObORXdywHyAcDA3ZZCfVq7FFXRx5447eaLlCSDm8SzX8vXlX2fzlZsTCphMaSZF6qKPnHjOV+ajyFBQr6+f03ZH1cURQygcotfZS6W2alqSLS87L6XP86M7erjxr6mDX7c2DaOWZ3J4cH6Be4MfAfEMwqpjdA6tZ4cndAnkaxVsbMjnxcZjVzD+JwQ0isxEyOCJQKF29oqkcCTMW71v8UTLE2zt2cr5VecDQjxwrO3SJsIXDPPsvn4uWlo0L8WzJzCGehLxXGVU8aqngciqmKJWUyS6vgoWixyPadBn9dFr9WL1BNGr5j5RHvIMTSGer264mj+d+yeW5S3DHXKnHLusKljFkHsoMY7a7O0lEA2JiW+2DlR5GCKWCYrnfqF4ngbxNtYEas6CRZfGrEcmEBHOQShZLQK3csvmpHguzFFwwdIi4QtdepJYaKglgwit0TIODThZUZbL265Soroqka+y8OIppHn7iDhWjb6IWtduWHgpls9t5taxr4FcDVnZZJzAgLo5wTnIaGYWBmWB6G787mHxnc0EdT5oiqEglqFSezZ0vCnIj6Kp/s5A4reTkqCICTNmi0Jl4ew6UuPq+piYwq7NJ0dbkrxObhk4xDi7RF1Cv7ufXlcvlTmV4mX59HkOgXAAh6NPFDCWfmpc9T0JO4Z28MDBB6YsH/GOcOULV9LjEAHHVTlVCZ/nUCREh6ODel09+cp8TB4T+4b3clrvflj7NdBVYvBZyc7MRiqRsmd4DyPeEe4/cD8D7gFqc49e5RjPwZgJg+5BilUie4VIOG2xRa/QHxObso8L/GP+WfMEHY4OStQl1Ovq6XEK4jkajdLr6qUsKwcG9wESsl/5V0o1JbTZ28j198PrP4Xz/o0oImjyi0u+eNzO50Rgz/AevnfS9+h19mL2plY8gyh6xNeJ21a+0fvGuLXTPFGmy8bmCc5KpGDxBOdsGzcRh0cPJ0JL54J0IjWAJYYlCf4EAG2RENb1xNTh5hZBNgOsvF7YtR5HdDlmTzy3WOcexHys0VCo4YhpftzG3h4bZzTkI5VKCEosFKuKaTBUEI1k8H5vM7u6hfXahvrUv+mJMKhlWNyzUzy/0PECP9j2g3kd88cBMxLP3/72t3G73RiNxsTfPwJaba0JxfPqgtX4xnw0WZtm2Gp2cIfcQvEs007xQZ0zEsSzJb3HMxCW58CCi+Av58PZt4sBlncUXCYYPswR6xE2lW8Syu6+nSIQJuX71cxIPNv9dnRyHd9d/V02d22mXDueaj1R8dzv6mdF3gourr6YbQPb8I35uOnNm7jxzRtTP7xbX4WFlyQvq1wvWit1leLYUkw27H47uYpcsd6evwjiOa9+itVGkapoTornQe8whXIdUokUnUI3ZUAajoQZ9Y0mvNXWl6xnl2kXIaUOvKPYAqk9nidabTgCDkq0BtHi5hTnJsuUUqrLxtn2ASy5Cq57Gr61VyiLXrpZDPLma7UhkYA6TwTFqCZc63LNOKn9EWLYGUhYbfw94ZWDJlaV53LNyWU8t2+ANZXTh7e6gi7UMjEZX5G3Au+Yl88t+BwgbC4ys2ZHPD+zt58fPz8+aPnpc4e5+L73eLfVjEaRic0TxOK3cHbF2dgCNhrNjdTk1lCSXcIj5z+Ku/9KinKmfh/yDDE4e3/g/UTBxBFwkCMTHs9F6mRSo0Rd8pETzxKJhDJtGQ26uanmU137c0WPs4didTHFOdppied8ZX7K7pteq5dXDpnYctjEXS81ERgTHqrRaJStzcN86bRKDg/Mb3A1aPdRnHvir7kyfTa9s1DtH+x3EI5EOdBvZ/mELoHT64zs7Jq+IPDdJ/dP2xlg8wR5rml+hP3fK65ZW86CE2hpNBerjQ5HB1q5lp1DO2myNHFasSj6FipnoXg+Ctz23EEWF+dw2coSXP65K1E8gTGU8uRCo0aRxWHFCkz114gFEomYQM4i/LDf5qXP5p23x3MqxbNEIkGeIWdZniATUymeM6WZbCzbyJt9bzIWGeP2928X6fUrroF/eQ301eSGRiZZbZRO2U8c0WiUjhE3tXkTiOcV1whys2CxIPj+dKYgnV1DwgoOICe1CGFGDOwZVzwb6nDIi+h0ZXB40MGpNSLcyXPqv8LwQVh3Ew9s6+TJ3ePv0zXqocaoFkX7MR+UnoSxeiWjASnuwBjklJJ1HAsgR4XhQ1j05WmVddNixeegeqP4f20xrPuWsLZIESwIwmpDniEfJycnQqYCoim7F1PuK9uAxT8LhbS9N6lQ4AyIDqwk5JYnFM8l6hL2DO9BI9OgyhKFnpm6m17oeIG7tv9E/Db1NamtNqydvNL2HI80PZIUKgfCmq/N1sbTbU9TqimlUluZIJ477Z0UqYpQZinJV+azb2Qf2qiEnMVXgUILeQ3IY8rHHHkOHw59wPWFp/HHxj9yy0m3pB9ftbwKv18HL8wcHD+t1UYkIgJaX/ouA83PUqyOfbev/xTe+HnKTY5F0f7jhO+89R12DO2Y1brvD75Pg76Bck15gni2+C3IMmRofU5RJLnwHrD1sChDQ9PoYXji88KPvPQk9gzvIRKJJJ5xn0R4Qh5GvCNsKN1Aj7OHEd9IUrDgRMQzWBpHG3mn/x1MHhP377+fLT1bjuoYMjOkLCnJYV/fzAUQizuAcQ6K51e6Xkl0dHtCHlwh17w8zUd96RXPS/OWcmj0EKFwiIcPPywW1pwlbJ38TtGNkhPjYFRG0ZFyHNHp6JwV8bxQvzDhcQ5iTusInPix9oqyXPb3zX3+NuTw4QuF+fcrlvL98xoY8gxRrC6mvkCL1VLK1556ihv/upevnF5NhnTmjgSDSo51llYbHfYO3uh946g7be3eINFolPYRF3t6TlwBcEbiWaVScfPNN/PZz3428fePgImKZ6lEymU1l/Fc23PHZN+uoAuNTJNISZ6LynYK9NWi3TAcjKWIT4NNP4EvvSIUIUqjaGdzmQj7rLRaWzhLXsigqx8G90PJqtT7MNSAdXqrDVvARq4il/Ul67mi9oqkJOOJxHO8pei04tP4YPADHjj4APIMOVKJFFdoEmnmHIKgWyiVJ2LNV0SYTF6DGFSmaK9MKIvtvaL657ODsUEovg88Dk0vACm8kmbAUMBKcax9LdWA1Owzo5fryZIKdViOXPivNUWDhNxmPEFPSi/ZiVYb7jEH1Yb8KS1utXlqon07xSSp9CTxQLngPwVhvOsBcA3Nz2oDRHjP4F6hZIlDkSMeYh8xTM5JQUOfEHiD05MRLcMuVpbr2FCfRyQKJ82CeNbEwiMvr72cf1//74nJkUamISLxzipc7fCAk1bT+LW2q9vKOYsK+P55DRTlKLB4glh8FvKy86jUVvJ6z+sJX0anL4pWrk2rzI57Vzkw0AAAIABJREFUJfe5+giFQwTDwcQxTkaZpmze4YLHEj8/9edsKN0wp2108qOfPLU72qnJrYmpO9OrfFcVrEppTWR2BVhTqeNrj+7hqT397O0R96LdPTZU8kwuWV58VIrnVKr2441yvWpG4tnsCnDl/dt5o3mYQwNOlpWM30/XVOrZ3S2+l0gkymuHTYTC436ZfVYvz+wd4PFdqRVcwbEIX390D9Z5WCf8PePzp1ae0HCdAq0CizvIV/53NwMztMQ2mhs5ufBkNpZtZF3xukSuQ6GqkGHP8SP8Xm4c4meXLkYty8QbHGNswu9sNvAEw1MUzwDVxkn+g6u/KJSU08DpD+H0j9Fv9c7b43nIM0ShqjDla7W5tWRnZk/xeI7jzLIzebvvbTrsHfjDfkZ9oyJcMKcEckrRBkzJ4YK55Sn3A8L7WpYpRZdKta3Oh5v2CaJycJ8gnxPEc6mwtYhM/R72DO/hC698gVB4ksLNaxWZGfqY73DZyZiKz6XP5uXQgIMlxcJWqle9DL4tvKA3HxxKPD/9oTBmd0AU6VR5gASKViCVSqjQq+ge9YC2lMyPK/FsasSiyZ8f8bzpp8ldkhu+BzfugkWXpVy9WF1Mg66BjMk5MiAKLKq8WQcMGhSzDN119I0TMIxnTiRhQsGiWF3M9oHtVGnHSZTpQsRBFGwO29uEPY6mMKXiOfr0Dezofp1oNMoB84Gk19psbejkOpqtzQnFc9xeoMnSxEK5EUaayVfm897ANup83nGP8bwFyGIktU6hYywc5BuDXfzklJ9wcfXFqQ844IaXvwvrbxZBmTMEx0/svJyC/7satt4OWdkMDTeOE8+tr8LOB8S8axIMilkWDeaIbf3bjl7UNQ+02lpnNYf85a5f8kzbM1y38DoqcioSKvJ+Vz9l6jJx78opEZ0HZ93GIksvTQPvi67TNV8G4LXu17iw+sJPtM1Gm62NmpwajNlGItEIHfaOtCGYFdoKelw9iethS/cWWmwtx0Q1u6pCx77emUk8s3tuiufn2p/jL4f+ApDouJrP793qt6b1eDZmG1FlqXi0+VHu2X0P/jG/4ENGW0SXt7FO2LOeAHhDXux+e+qC4iSoZWpK1CUJu407P7iT/zn0PzNsNTPmKjAo1yvxh8JzsnMD2NtjZ1W5jjyNnM+fWsmAe4ASdQmfO7mMr6w5m3NXe7hhfRVXriqZeWfEFc+z6/Dsd/ejzFSyuWvznI55IqLRKOf/ZhsX3/cel/52O7/ZeuJsT2b8NdbV1fHyyy/T2dlJV1cXXV1dJ+K4PlIEw0H63f1JVZsr6q7gte7XaLcdfQqnJ+RBnaVGIpFww9IbuOrFq2g0NyZe/2vzX2fv35JbLpS7SsPMCbfa4vE2QpUxpngeolepRZ+hoOHFf6Xf3ikGTOlsO2bj8RxTPAPccdodnFF2RuI1vUKPNSDIgH63SO9dVbCKFmsLT7Q8wffXfJ9CZYpJ4sBuKDlp6jmWnyIGvHkLxaAxheLZFrCJ43H0w2gr+O1iYhINwyu3CpVw93vCasMz+yLAQMhFcUy5qVNM9X4zeUxTJm+rC1azJ2DG7jOjlWtTGvDnZ+cnHlKBiJt6Qz7l2nJabC3c+MaNtNvaqclTorEcSE63lynh0w/Btl+BZ5RAzJduzlj+WZH8HQsjaR9xE5VrPjZWG/mfMMWz2RXg4vumD8XojIUTFOVkc8P6KtZWT088u4PuhIKlVFOaRJZq5VqCEc+sFM+HhxyYnH4c3hB2b5ARV4DvnF3P9adWolfJsMaIZ0O2gTpdHVt7tiZaNgcd/mkJSY1Mw3WLrqPP1ceAe4B8ZX7aQfI3V3yTT9VPT6icCCzNWzoeQjpLHAvFc7tNEM/5WjnDk9SdkUiUHosgn04pOoUPBj+Ysv2oO8CNZ9Xx5i1ncM3acj7oEJP2377Zzg3rq6g2qhh2BnDNwwZg4CMinisMyoQNTHAswltHprZTP7WnD7U8k3vfbMOgliURVJUGJcFwhPYRF59/cCdfe2QPjf0OfMEwPRYPb7WMsLwsl6f3DKT0x352Xz8ZUglfWDn9tfhPHF/IMqVs/vZ6HN4QB/unv84azY0sMy7j1jW3JoVZ5SvzGfGOJAV1HSsExyKMRaJoFZkifFGeKdStc4AnhccziIDBTvOEAuLK68a9dNNgwOZDr5LRZ/PNy+PZG/LiG/Olbe3NlGby6zN+TU1u6hyQNYVraDQ3snt4NyDUWgnklKLym4TiORKOqZTTT8zaY2F9aZFTIlS1lnZBPMeD7GRKIcTwmCE8Bu/8Z2Lc2mprZe/IXv7Q+IfkfVk6xPg2/owqXoF1/e182GllX6+ddTUGSnTZovihq8QTGKOx34Et1qLdY/FSqssmM0MqiNO8BqFERdimdFs8McXz8VPeHxWGGjmSmUGFtuLY7M9Qk9ZLtEHfwMMXPJx+W6UhPfFs64anvjj+NrNRPAfcEPIndfG5gq6pxW6VUYQPBtwJq424zQbMbLUx6hulL+TEZawV8yi3GMfb/Da+/vrX8ZoO0e8aIBQO8Nmi9cl+6Ijf5g1LbyBDkkGZpoxzK89lx+AO3h98n2ZrMwut/fDUlyhQGNg7vJf6MOP3g0mK5wZJNkprN59p+Mz4uMtnh6HxuSYf3i+CSpd9GtSF4JremqpQVciwd3hqR2rIB93vwQ2vw9k/YyDip1iuB8eAKOg0nC+6TQF6P4SgeK7rs4XHc6oO1z81/mle2UrekJfvv/t9dpt2z3nbo4Er6MLsM89KhPBW31vce+a9LM1bSpm6DJPHhCfkGfd3ntgJUnMWS0a6ebn/bb6eb8A75iMcCbO1dyvnVpx7nM/q+OKI9QgN+gYkEgnl2nJ2m3antdrQK/SEI2F2Du1kVf4qHjz0YFJh5miwqlzH3t5ZKp5naV0VjUY5Yj3C7uHdWP3WBKcxH5HKdIpngKXGpdx/4H5AFI0xNoC5FczNwpLqBKHb2U25tjx1QTEFluUt46D5IH2uPl7reW12BcRp4BvzceEzF86JfJZIJCwvy52x8OALhnH4xudPe3psrK4Yt6IadA9Soi4hX6vgykUbOGjZx9c21sw6iNKgkjPqCc7KqqfP1ccXF3+RlzpfmtW+U2HI4WcsEuE7Z9fzu2tX0TYLodqxwozEc3NzM48//ji33347P/3pT7n99ttPxHF9pIgTorKM8RtMoaqQm1bdxI/e+9FRT14mtsnfuPJGNpVvot0uCO1gOMjdO++evQF9pkwofaex2UiJmM8wLhO9xcuo9LrI1RYTDgdwlqZRO4No//GMJgYOqWAP2MmVpw5G08mTFc+lmlIUmQpW5q9kY+lGyjRlFKgKpt44Joa+TIREApf/HlZeG1M8p7ba0MlzxcTE0gEhr1DwqvKFVcUF/wHv/Ad5yjysfitjnW+LAJ8ZMBjxUxQjd1Mpnk0eEwWq5ECP1QWr2ePuwTqNZ1N8khwYCxOReGjIL6RMU0a/q58WWwvvDbzHyuwRrGElVz/WkayuymuAW47AN7YT0qRXEqVCYCzMOf/1Du7lXxIBLKd9m8BYmHN//Q4dTikely1BgH1U+CRabWgUmfTbfNM+UDrNHqpjE+yfXLxoxvAuV2j8HjIZ6iw1gYiPbvswN7z25bT7CIUjtI+4aSjQ0DriYn+fnSUl2kRbkEElx+z2Y/PbMCgM1OTW4Aw6qc4VirBWk4syffowrttOuY1rF16LK+hi1/AuFhvT+MYjqvap1P+fBOgUuqNueWqxtlCbW0uFIUZQTMBrh01841Hh37xAvwBHwDHl/mh2BcjXyKkyqjitxsj7HRb29dpoG3bxqdWlZGZIaSjU0BQLGIxGo9zxwR2zGugN2P0ficdzhV5JT0zxvPngEDf+dW/SNRSJRPm/nb386jPLaRp0sqw0+ZkjkUg4qULPV/53DzqVjIuXFdE16uFve/q44vfv88zeAb56ejX5WjnvtU8lOd5tG+WKlSWzapP7J44vavM11BWoGUnh+b1jaAcvd4oU90ZzI8vylpGryE2aqCkyFaiyVMelrTvuzxwnd7TZWXMKGIxEovhCYZQpJig1eSo6zOJ+cP5v3qXfNrP1TL/Nx/LSHDyBMTIzJLOe+MRh8oqC+XRKutNKTiNTOpUoB1FwrNfV81jzY2hl2inEc7Z3UByb3yI8hLPSP8/jnsnTwlAjBAUu07jiGcY74Kwd8P598OczYeQIQ54hrl14LY80PUKw8Qn44wbY+7/Csi6udo6hTC+I5m+fXYdOJaM4V8FA7DvY3WMjCti8QiXaNeqhOhZuStUGuOx3if3UF6h5cnc/PmURWbPxIz7RsHYS6X6Pdz09nF56+szrHwOk+/0AMcVzGp/nwf3CPzr2LIgH1E07YY8TeRN+0/GcjCRIJAl/8Lhqb6IAaSbi2ew1I4nCkWw1qAsSVhtdji62D27nd9t/xgeVJ7E2dwGbhrt5p++dpO1bba2cUnQK92y8hwZdA8ZsI3dvuJvvv/N9NndtZpHXDWM+8gf2MRYNU5e3ZPyc8hYgdwq/W51cx8JAUChnQz7Y8QfR4brj9/DUF8Y9l3veH++gyCmd0Z5GniFHkaGY2pE6uF8QXFnZkJHFkFxBsc8N3dug6nQ46QY49LR43yeuTZDQqiwVWpmWfnd/0u7abe3ct+8+Hj/y+LTHkwpberbgCXnmFhR/DNDt6AZmJha9IS9mrzlhQZmVkcVF1Rdx5447efDQg6wrWZfsfS9Tsax4LU8OmAhl5/JW31vsG9mHXqFPKop8EhEnnkF4OFv8lrThgnFyet/IPq5fdD0Wv4VP138ai99y1Or2leXCbiGSJqA7DsscFM+jvlGi0ShnlJ3Ba92vMewdRiqRzmsMYvFZ0nYYgSBwJUhYZlwmiGdDjSjQmQ6N+zufAMzWZiOOpcalNJobeeDgAzToGo66+6HFKqw79o/sn9N2s7HbeOj9bv795fEw40MDDpaVjs9ZB92DiS6PCm0F/jH/nLrssmUZZEolSaKF/zvyf0miVBBztz5XH5fVXibOd3Ln1jTwh8IJkU1jv51lpbmcs6iAjXV5sU65ExPGPiPx/MgjjyT9PfDA1ECEvzdUaCq4/+z7pyy/qu4qnEFn4gEzX1j8lqRK+8RW7SHPEFGi9Lv6020+FfrqaYMFUyIe4OEeoddQTrnHjuSsn1IskTFUME2FTJohbmSmxrSrxK02UmGyx3M8ePD2U2/nBycLs/SJ4XoJ9O+G0tWp37BouVB+56ZWPFv9VnSSTOFTrCkUpLNEAmf+EM74ofBGHmkmS5qFXqHH/Mr34NDf0n8GAGNBWjOgzrhk/LwCtqTBb6qAnpX5K9lnb2E06Ej7IDFkG3AEHbSMmJFIImjlKlRZKjZfuZmbV93MfvN+TonsY9iwlg6zZ8b249misd9B24ib/b123rcoeXtQQp/VRyQKzza52Nfay71vHL3i/2hg+gSGCyqyMlDKMtL6N4XCEfrtPioM6UncyZhotTEZUokUVZaK3Nwhdpo+TEsuto+4Kc7NZkVZLi0mQTxPTN81qGUMOi0os5RkZWQllM7x/z6zr5+Llxal3DcIdW52Zjal6lJe63qNxYb0xPMnGTq5LtHFMR/0OfvYb97PaSWnUaRV4PKPJSmTXzlkShBuUomUtUVrp6ieR90B8jRiQLy6QkfTkJObn9jPrRcsQJ4piKeFRRpaR0RV+4D5AE+1PsV/7fmvlMf0QYeF7z65n5+/eBiTw0d5mt+mI+A4OquoaVA+QfH88AfdeILhJOJxf78dRWYGZzbkc1qtkRVlU585a6r0+IJh7rp8CXX5GjrNbo6YXORmZ3FowMHp9UbW1xrZP0ntEIlEeb99lNNq/zEyLT4JyNPIMacgnt/tf5cfb/8xP//g54Sj4UQ2x2Qcr4BB96RgQK0ia04DeE9wjOysDKQpChxleiX9Nh++YJgjJhdbm8Txp1Lox9Fv81KmV1Kqy56fv7N7aqfWXHFK8Sn0ufrYULphEvFchswziCc4RpZnaFp/ZxjvBJoWxjqRSyJTCuJrwnvhiNmrVZwm8kF63sPkNrHEuIQSZSHtW26FsrXQ+Y4gqPXJKu6inGx+fNFCrl0rVMAluUoGYx0pm1t2s7Zak0Q8VxpixypXj3cXAt88s5ZyfTZ/OhD4eCqet/6Mw6s+g06hTwoD/8iQxqYCAEubsN2LEdOyDBnZGdnTB7Xb+6YEQXpCntTWXzGf5ziJkGS1MYPH86jXzNJgiCMEYopnE0SjmDwmTi48mVedLfyn5wjnl59DnXOEUd8ojoCDBw89yLNtzzLgHqAqp4qzK84mK6YWX1O4hucue46bVt7EcocZrvwz+W1vAFBfdfb4mxvrxDUV9HJKwRrOsg2La2C0Vfgs7/4fEdDuGRXzKRA2jfkLYydXJgjPGaBT6Kb61PbvSgiDQpEQo5IoBfZ+6HpXFGFKVoGlE4YOiKyYnX9O2OAs0C8Qdglus1BHAw8dfohLay7lhY4XEh6ws8Wzbc+y2LD4uHr6p0K3s5tMJFhn6Iput7dTlVOVVHi59eRbOTBygNUFq7ms5rIY8Tx+b5QsuIiKcIRLF13Dq92v8nDTw2K9TzharC0s0Au+IU7Ep1M8gyD0lFlKziw7k/zsfNaXrKdOV5ewa5gvjGo5RrWcpqHpu3otnsCsbcbipPr5lefzZu+bDHuGqc6pnrfHczqrDYBzK87lh2t/SHVutRiPZ2WLkMHWV0+o4rnH2TOnjpllecvY0rOFvcN7+c6q78zrs5mIJksTWdIs9psF8TwWGZuVFYsgnqdXvLeYnIkQwmg0yhGTkwWF43PwuNUGiCLJsrxlNI6m58lSYXLA4ONHHufN3jeT1rEH7EglUvKV+RSrixP+8DuHdvLQoYem3f9PnjvEozvE+gf6x4lzqVRCTZ6a9pETo3qekXh+/PHHOe+889i0aRNnnXUWl1xyyUybfOKRIc1IOfCWSCQsMS7hkGVmNex02NK9hfUl6xP/nqiYG3CJxNPJFeBpoa+ZO/GsMor2RIWW3qwsyiNA/XmUlJxCf8ny6betPF20VaVAOBLGHXSn9WuNE8+hSIgR70iCmC1SFyWsA+LtXAlEwjC0XySWT4ec1B7P9oAd3ZgIdhHtjzGCYsU1IkhPWyy8zTwWCjOUDDt7wTqDpYzPRrNcnlBxyjPkZEmz8I6NK5JSWW0Ys40YsvN4IyuKLo3CM1OaSX52Pjv6G8lEnVAelWpKWZ6/nAPmA2g6X2bl+V9gYZGGTvOxUSF/2GlBliFlV7eVP7/byTN7B+i1elhXY6DXk0luhm/GB/MxQcgvJpKT4A+JVpc4wfZJgvDuneoh9bv9v6Np2EShVpEgCGeDiVYbqaCVacnVCWVVula0w4NOFhfnUF+ooXXYxd5eexJ5p1fJGHKNJAY89bp6tDIthapCBp0h2obdbFpYkHLfE1GmKWPX8C6WxIo0f284WsXzA4ce4OqGq9HKtEilEqrzVIlrOjAW5u2WEezeYIJsOqngJPaN7EtsHwpHcPnHEiRTtiyDVeU6zmjI57IV423suhw7LcOCBHqt+zW+uPiL7DLtSk7EjuHtlhGsniCyDCmvfmdDWgX+o82P8pu9v5n3uU+Hcr2SXquXxn47I84Aq8pz6ZhgO7CtdZQzGvKQSCTc+9mVXLt2aofHtWvLefqb68jJzkp8rq3DLu64bAlPfv1UtIosKgxKeqzJ99CmIWdM4Xjild7/RGqkI55HvaOcVXYWzZZmHjzvwbRKykJlIX9r/Rvfees7x/S4XP4xNIoJxHN25pyIZ28wnNJmA6AkN5t+mzdRXN7aPMKvtrTw75ubU64PQvFckptNmV45b3/nyQXzueLUolPJlGayrnhdcuEzpxSpa5DgWAS5tRkMtdPup8/mo3yarhpA7GO0FTSTfCX1VaLDzdwixn3Fq2Bgn1B0KwtZGJZwpHw1rPq8EFKkUDxnSCV8eUI4UIlOfB+BcIBXLbejLdyGLVZQ7h71UJWGJFdkZfCTixexx65K2C98rJBbztu6AjaUzS3f4LhBWyI6FFMhbvVn6RDrhHwYsvVYzNOEvzt6xxWkMcRD3qcgNo9QZikpUZckWcqkstSbCLPXxPqIjGZHpyB/srLBZ8PkNbFAU8EWk51d1+xiY+1FSO191OvqabG28GLHi9yx444pnbZx5Cnz+EzN5ci8VihZTf6qL5MZjVK58MrxlTLlBLRVYDrIVfqlrFMUQP4iOPKyKMq8f59QP6/7FjQ+LkjegHv8c8kpTSncmQy9Qj/1M5hAPA97hjFkqsjqelcQX7XnCI/34hXiGBouFOKfdmEz0qBvEGPUd/8T3v0lNr+NN/ve5F/X/Cvl2nK29W+b8Zji6HR00uPs4TMNnzkuRcbp0GVvZ1EwhM02/dyx1daayI+KQ5Wl4ulLn+a2tbeJ+Z5jINmCaPEVcPnvObPqPD4c+pA2WxtXL7j6eJzGCUWXsyuRGVOuKUeCZFqCtUJbkfCG33zVZqpyqligW3BM7DYuWFLICwdS33O6rAGe2NXLqCuIYZbhgi02QaqvzF/JodFDDHmGWKhfOGfFszfkJRwNp75XxVCkLuLy2sspVhUz6Imdg7EBbF0nlHge9gzPaexQk1PDxtKN3H/2/VTlVB11R9phy2EuqLogMT96q+8tvvfO92bcblWFjoP9jkQoeyq0jbhpG3ETiUQxOf3IMqVJ6vdBz7jiGWCZcdkUtfJMMKjkWGLjCUfAQaejcwrfmLDjQYjA4m4J2we382z7s9Puv3XYxbY2UbBt7LezfEKXaF3+x4h4fvLJJ3nkkUfYsGEDv/jFL6ipSe3r9o+CJYYlKSfqs8UR6xFsARunFJ2SWDZxMBMnnOekeM5fmPSQcgfd03pER6NROkMuomN+0BTRFwlQfvqtINdQb1jIEdsMN/HK9aKFKgWcQSdqmTrt5E+ZqSQSjdDl6CJfmZ+o6k9EgXKS1Ya9B7L1oi1zOmhLhAXIJG84m9+GLugVAytj/dT9SCSQvwDMzRg8VkZrzxQ37GlgtnUQlkooUI4TbxPV3JB+Ave15V/jKY0KfWZ6JU+xupjGkSaUGckEfrGqGEk0woC9Ayo3UGVU0Tl6jIjnLitXrS7l7VYz2zsstI246bF4qclT8/NPn8JCvYSuUfe0N+djgq23w4Pni/a9CYhPqD+Jre8FWgUjk0LjHAEHfzjwB/525HmqZ1J1TcJEu55U0Mq0SOSDSJDSbE1NUhwedLC4WEtDgYYXDwzSNuxibdV4AcugkjHsHfcWK1QVsvnKzUglUt7odHHpimJkmTOHVpRqSolEIywyLJrTOX5SkCvPndLtMFuEI2Fe7nyZ6xZel1hWk6dOEKxvt5hZUKhFo8hMKOsa9A1JCg+LO4hOJUu6Lh74wkncfkny573D+Wd2Wl4mEo2wpWcLV9ReweqC1XQ6OqccV+eoh6tPKuOHFy6ctsNgt2n37G2h5giNIotsWQZ3vdTMF9dVUpevSQpa29Zm5vQ6oY7RqWQpLQUUWRkJm5DqPBWdo0LxvLBIw6qYur9cr0ooq+PY3j7K+n+qnT9WyFOnJp7NPjNXN1zN4xc/Pq1aqkBVwN9a/8a2/m0EwuP76XJ0cdt7t837uFIqnudgtTF5+4ko0ykZsPkYsPtYVZ7Lvl4bf3ynM9n3eRIGbD5KdUrKdMp5KZ6PBfG8PG85v9v0O4rVxVOsNiSOfnSyCIaWv8Lar027n/i5TAtNEWSpkm02AEpPht4dImQpr0GoLgf3ivPLzmPBcBvNeZViYm7vA9NB0aI8DZYUK/mg7xCPNr5MNKTnoGsz1oAg1rssHqoM6Z/j8swMyqvqkbg/hornc+9ix/AeTi85MTYbMyKnRNhEpIKlXaiSrZ3w1JfgqS+i97uxPHYFPPNVSNV6PGfFs1CFvXTFS0l2ebnyXByB1CG94UgYe8DJ6Tm17B7eLRTYmiJwDQkRSjBARskqJBkZYrnPyoLcOnaadjLoHuQby7/BqvxJNofNL8EHMcsW16Cw75BmULHuu9yz4jtkTZrL+PULRTD48GGRfaOvhoNPQcNFULBEhD0uuxoOPSPUx/kLx606cspmTTwnKROjUUE8lwniecgzRLGqEJpfhKWfBl1MAVl+Khx+Rvx30WXQJWxGFuoXClWitQs63+Gtvrc4tehUcuQ5bCjdwN7hvTMeUxzPtj3LpbWXUqouPeGK5y7TPlb7fDOSZ6221pRdOcos5bi90USPZxBe8UuuQivTcmnNpXx/zfeRZ3zyBDgT4Ql5CEfCCZFahbYCvUJPljR9sfS04tO4tOZSgMT5N+gbaLakL8TOFpevLOGF/YMpu4le73Dzn6+2MOzyY5wl8RxXPOsUOgzZBt4ffJ+FhoVztpMYdA9SpCqaVYhkoapwvAPRWAcZctBVzun9jgYjvpG04ZCpkCHN4Fdn/IpSTWmS33unfeqcJI6xyFhafqvJ0sSn6z9Nl6MLb8jLtv5twkVghrmZVpFFTb6aA30OHtreRdukfKRwJEqH2Y08U8qA3ccRk4uGCWpnd9BNMBxMsphdnr98zsSzcULA4P6R/TToGmgabUqy9+1z9VGqFveGWl0tbfY2QAR1djo6sfqt9Dh7ppxzNBqla9TDh51WQuEIjf3JViG1BYJ4ns88dq6YkTXQ6XTk5+fj8XhYu3YtDkfqh+4/ChYbF3N49PC8t3+u/Tkur708yXx9otXGgHuABl3D3Ijn1V+Ec+5I/PP292/nxY4Xp6xm9Vt56NBDfPX1r3L5y1fzSo4eNIX0uvsoqz0fEBOGGf1xKtaJVq2xoEhNfWg8MTkR5JcGEokEnULHQfPBxMUzGQWqgmRvHEcs4XcmSKVQshIGkgcqVr8Vnc8lBlZ5C1IT2HkLoO9DjC4zozUbwNo97oOWAs3mAyxEnvQw0Cv0SZOsVIpngIuqLuLcUAZlWalV4SCI5y5XCxpZsipaAixHzoHZ+1MpAAAgAElEQVTyVZApo9qomnYSOluEwhH29dr5xsYamhzvUFXeRdeok65RDxUGJXpDHhkBJxV61fEzod/6M0E4N78E5/0bPP//hCIjhj6Lh69nPC9+d6aD0P7G8TmO44ACrXyK4vmA+QA6uY53TS9NO2FNBXdoZsWzK9KNMlLH/+55j5EUauv4g2dRsZYyvZJHv7yW3AlEhV4ln+ItFvdh3tHn5cJpbDYmolxbTlVO1bTH+0mGLEOGJksz77RqVZYqyZooTjxvazPzo2cO8vUzqjGq5YkWrDpdHZ2OTsIRUQAyu6a2/ymyMqYMVC2BfobHdrNjcAc6uY7q3GrUWeqU/nidZnfCczwdAuEAh0YPpU+5PwYo1ytpG3Fx7SnlVE1Qgjv9IZqHnJxcNftOnyqjivYRN/LMjCSlQqVx3Es6jsZ+R4KY/ic+HsjTyDGnSP02+8wYlTMXCRYbFnPdwuuoya1Jmqh+OPQhr/e8nrie5gp3IIR6guJZM1erjcAYSlnqbhdttthv06CTunwNG+rzOH9JISbn1M8hjj67h1dN9+LJ2vmREc8Z0gzWFa8jLzsvmXhW5EA0yk8yHsalqZ22iy0ajdJv81Kim6HrQCIRhLF20jGXnyo6p4abhPorfzFjtm5GfaPkj7SxUKan2TcsAvDyGsB8ZIrVxmR0+/YyVvhr7tt3H2t0V3FJ9cUEVW8yFo4Iqw3j9M/xk5cuop25ZW+cCITCIVptrR8fOyxtcWriORoVc4668wS5ajoIfgcGjxXLRb8UwXlxG4mJsPeI8PEJcAdTeDyDIGhjoofJAppceW6yuMQ9xLWbrwXEs1wrzWJx4RrOLDuTL7/2ZQLqPHj7bkx971PoHhUB6SAsC7XFLMzO55m2Z1hiXMJXl32VH5/yY+FVPrBX2FQ89w3Y96jYZsI8KCNTxqYVU/M7fPqFMLgvRirHiGdrp5gXffohYS2oqxCE8/b/HrfZgJjSe3bEc9JYp+MNyFRAriCYmyxN1BmXig6DjbeOr1d+KkQjUL5WvK9ZtMA36BuEOMLWDeZmtrS/yDmI66hMUzbrwvZYZIwXOl7gytorKVQVnnji2dHJ6qJTsIR9oks3DdpsbVMUz0mIRKYNXf3xKT9mU/mmoz3cjxzD3uGkwPFFhkVJgcCpsCJ/BVfVX5W0bHne8jlbGqRCfYEGnUrGh11Tx/EHhnyMRaL0WLyzttpot7VTl1uXOMb5Kp7jmWOzQbG6WHg8g3imGevFveYEYcQ7N+J5IuQZcuQZcix+C1e+cCWeUGpB3Z8b/8zv9/9+ynJvyEu/q59FhkU06Br4YOgDtg1sIxKNJI9B0uCUagOvHjLxi1eO8OTu5HtOv82LQSVnaWkurcMuWk0uGgrG+ZtBjwgWnDjnWmpcSrO1eU4ezHqVLKF43jeyj7PKz0Ir1ybsNCDZorY2t5b2mLVPu72dSm0lr3a9yuXPX067vZ1+m5f/fPUI/7ezNxGCXKLL5ldbWinUKpLmQXX5Gt5pMbPhl2/hDx37EO6JmJF41mg0bN26FYlEwuOPP47VeuzDWT5JWGRYRJu9bU4/ponYO7x3iqogyWrDPcDaorVzs9qQZiSlRw+4B9hl2jVltfv23cdO004urr6YB897kF/lqrErDZg8pgQJvCxvGQdHD04/CcvOFQP9gd2w64GklGS7P32wYBx6hZ7nO55naV7qdPZC5SSrDefgtMnnSSheBQN7khbZA3Z0HquoIC++HM69a+p2+Qthx/3kacsYlUQJEqXXfDDxcigSSqqyNdlaWJiZTBzX6+o5Yj1CKBKi096ZlniWSCTck1HENXknTXktjhJ1CSOBDgzZkz7LV39AvctGR9lKAKry1EkqwPmiechJSW425QYlmoL3cSifQFO4jffaRkWrq1wLASeLirUcHjxOxafeHVB3DnzhBVj7dTEp/csFCQW7v+sDrnY8KNTozS9C45PH5ziOAwq1CkyOZPJ3/8h+PlX/KfxjQRSaOVzvTO/xDKCVa3GEzFhGGrCNdbOjK/m+HQpHaBp0srQkB71Kxgs3rqdmEtGoV8mwB61TWt+GnX5G3GOsTOGpmwpri9Zy/aLrZ3lmn0yUqEsYcKdRaE2DYe9wUtcECGVu85CLW548wH3XrOSsBQUY1XJGY6SbKkuFQWFIEL4T/Z3TwR104x1zE84c4td7fsPnF38eEEFg7mByIWksHKHPNrPn+EHzQWpyaxiLjKVVgR0tFhZp+cYZNShlmVQbVXSNevjvrW1c/rvtrKrQzSk4TSnLpECrSPJlAyjQKHD6QniD4/f3zlHPlOvhn/hoMdFqwzMhgMXsNZOXnV7pHMcVdVdw68m3Cu+9CUqUg6MH8Y352NnfNq/jcgfCyYrn7EycvrkQz+mtNiQSCSW6bD7sslCiy+a316ziJxcvSllIjKNvbDO9niPsdP6FK06enrT1hrxT1EPpxi3zgTHbiMVvGVfRSCTQcAEKaZhDDTdNu63dG0IqlZCTPQu7EGPdVKsNlUEQdSOHIa8eMmWY8xvQZ6rIan2FBfUX02ZrE2PdomUgz5nRss7qt1KhbiDgz+Urqy/h2oXXkpWzl26LA5c/NGPw8caFxdycMT258lGg3d5OiboEZdbscyaOK9JZbXitgnwuWyvC6goWwXXPYFhwOZYMKdSdLQjbyejfIxTvMQTDQSJEkElTFGYq1gniN0WAuipLRTASJBgW5ECLrYUWawvRaBSzz0xeBCSFS/nByT8gEA7QpS+HkBeTvZPCwUNJvt/klrNAmo3ZZ2Z5nrA3lDj64TfL4MVvw8vfg3N+LgjZgFsQ8TPMg/z6hdC9Hfb/VaiK49YxJatBUyC8x0GonjvfElYcieOZncezXqEfJ9+DHnjpu3DRPQnl9L6RfawqPgW++lby9VR2spif5S8WhFiMeC5Rl+AJebA5+3BUrmP/8B42vHMvOIcE8ezuIxqNsrVHWHP8rfVv3PDaDbze83rScTW5mihVl1KZU0mBqoAR70iSUvB4osnSxEjQyYpl12GVSomOpFfgttvb0+YQAOAZEQW6aUJX/x5g9pqTSEpZhozzKs+b835qc2sxe81HHfANcHqdcUrInNUTxOQO8c0zasiUSmYMfo9j1D+aOL/49V2VU0UkGsEbmjkkOI4+Vx+lmumzEOIoVk0gnmvPgY3/Ouv3ORaY7VgsHfQKPftH9hOOhtOSxe/2vzt+jhPQZm+jKqcKWYaMry3/Gj/Z/hNUWSoW6Bcw4B7gtvdu49m29FYUp1YbePiDbqqMKt48MoJ/LMIbzcNEo1Haht3UFahpKFDTMuyixeRKmkdMDBaMQ5WlokBZQLeze9bnb1DLE4rnfSP7WJm/ksWGxUkuC72u3gTxXJdbR7u9HVfQhSPg4JKaS/jN3t8wFhmjx9nDb7a20W3xcOdLTRwedFBlVLGuxsif3u3g7quS+bcFhaKjVFl5L0GOr+XGjMTzXXfdRXFxMbfccgvd3d387Gc/O64H9HGHKktFsao4IW+fC8YiY3Q5uhLhXHFMtNoYcMWIZ1f/vCXvw95h9o4kq379YT9burfws3U/47Layzip8CROjyr4Uag7yfJCp9CRl52X8I1Ji5XXw+bvQ+MTEHQlgiKmCxaceL6HRg/xuQWfS/l6gUpYbSTO3zkwtY0yHWLtlHGEIiH8YT8aZyzIRq4RthqTkbcAPGYMxScx6htli7GY67d+PaEGvG/ffTzW/Fhi9WZnDwsVyTfYRYZFHLYc5pWuV7j6pasJR8PoFaknMhKlEak3fRGnSFWEHxPFmkl+V03Po1l5PZ5YFVMono+eeD5icrGoWBDpWrWfLy25nmyVic5RjwgWU2jB72RxkYamwePk8+wZhQUXi6KGRAKX/LcYGO8WCdhl7Y8RQQq2HvGXLvX8Y4iCHAXDk8iCRnMjK/JXIAs2MJY5N9WoK+iaVkEcf+3fzruKLJmHvzT/OlEZBTgy5KJMn41mmkGUQS3DPWafEoL5TouZVcXZZGbMbLMBUJ1TzafrPz2rdU8IIhHY8/C0HQ1zRYmmJOHPPxcMe4aTWnlBKJ7fPDLComIt62qEktOgliWIZxBFrhabmLgJxfP0ysZuZzeVOZUow4sZ8Vq4oPICANQyNe5Q8iBjwO4jXyOfkdTdPbybkwpOolxTftzsNu66fAlf3SBUiNV5ahr77fzPe53cddkS7r5q2Zz3V2VUUV+QfN1IpRLKYn7SIIIFp/Nr/XtHIBw46gDl44F48WVbm5nzfvMuY2ExgYtEI9P6H07G5NCXg6MHKdOU8aW/PpfSymMmuCd7PCuycPpnb7XhmcZqA6BUl83ublvCZsqgkuH0hwiOTSVVHN4QkZw3+e2me7l+0XVsGXx42ve+c8edPH7k8aRlx0LxHEecyJyYfcFVf+b+3FuwZk1PbvfPxmYjjjVfFkTbBBwwH6CjZKkg6+TimjcVLqBwLAxHXkaz+FPoFXp6XD1QuAwM1eO2A2lg89s4s3Ittyz9FWsrCyjTlpEVLuOxwy9RaVClDIiciJzsLH5/6exIhBOJw5bDibySjwW0JULhO/kZbWkHY634rtwmQRLLlBg0xcJLvGpjwsIhAXsvjPmS/MQ9IQ/qLHXq9nW5GgqXQu8HU16SSCQiYDAgyKluRzeBcABXyIXZa8YY9EPBYlEwUpdgWvEZuO5phmUKCoebxhXPALnl1AZDZEoyWZG/Qiwzt4hz+vo2uHEnnPQvQhQzdCAWODc98RzQVoHXAkuuEvMcfZVot8+f9N0uvlwsL5hAPOeUiveYYVykV+jHVZv7/yo+q1oRchiNRgXxXLBq6oYKrSCjMzJF+7/HDEEPUomUxTm1HNDoeLewljU+H0rjAhjYTZmmjH5XP/3ufm5++2ZMHhNburdQoi7hDwf+kLT7fl8/Cw1CwS3PkKORaY7aM3Y2cAad3Pzmt/mZ1UlO9Sbkkgxc7/4HHJwaUB8MB3GH3GmD5YlGYfu94n70d46jUcdORIY0g8XGxcdE9VyuV06xXfugw8KSgmwuX1nCqTWGGe/xIHgHT9CT6BBdnrecLGkWeoU+tUf6NJjo6TsT4gWXcCQsOoAWXTrr9zlaBMIBPCEPOsX8OwX1Cj17hoVwMCkbIga7385hy+GUr3U7uqnOFYW29SXr+VT9p7ig6gKK1cUMuAfYPrCde3bfw6A7tY/3mlj35J2XL8HhG+NX75m58a/7uOHh3XzQaaEuX019gYbDA04ODzqTrDYG3AMUq6ZyVNU51SmtDNPBoJIx6g5ywHyAPlcfy/OWi1y5CcTzRKueMm0Zw95hDo4KEdCawjUEw0HOqTiHVmsXrx02ccdlS1hSksNTu/upMqq4ek0Zv7hyKasrkrmpMr2SD390FuZAL4qM41v0SsscHDwo1J779+/HarXS2trK+vXrCYXmp/T9e0JVTtXcrDBi6HH2UKAqmKIqmGy1sVC/kExp5pxuTnGEIiHsATvukDvJruJ96/usKliVdKP/jrycgyEb5ZrkFrQV+StmtttY82URJlG5HmQaiCnehj3DM1a89Ao9l9Rckvaho5FpkEj+P3vfHeVGdX9/1bu06treq8va627c6TYdjCkBUoAACQmBQGhJIPmBExK+EAKk0DHNOJSAwTa4YYwLxn2L7fX2rt7bSnq/P95Ku7OSVtJ6TZxyz/E53tFopKeZefPeffdzL2uYEMlE8Zw7gyqehwZP7pAbCr4CLEdXXLgIA/pJAE8CTSFNYe8Sy+EJ+fC3w38DQBcEWuwtsd3b/CaUipkdzST1JDRYGrCzeyfun30/vrzmS7BZSW4xiYYOEJNAzNECLIJc+YgS4qEwEKm8IPbb5GSJYPMGGQqw8aDF6EaZTgpCCJxBK+bkzATh0RXHApWYhoOwWJhsEGFXiyXhpPeU4TEBI0umWSxgxk1A44eAsw/F9l3oz7uAlk3aO2PE8/OHnmd4dp5piJAIwpxehtVGKBLCUfNRTNVMhdMjBos7NpnfYG5gLES5B5OUiQ5BzpeDzWLj8qlTcGPF3ej2tOGL7uEJ2aFuZpBgIqgkfPjC9pjHcxTbTxgxM/cMUUaNB527gY9/wligOlXkSEeEemSAfm9/QsUziwXcumg46Eozyt+2UlWJE1bq82xKQ/Hc5mhDkbwINeJLsTz7zthCYyKrjVaTB8UpSsb7Pf1Yd2IdlhUsQ74sH51OunASjoQn1CNspG91gUoMm3cQV9TlYX6ZJubdnAmWVemwsDzelqFQJUbH0ISjz+mHTMgdkwz8VyJCIrj646tjqruJxraubXhs72On5dinAiGPAxGPgy1NRnTbfNjY0A+TzwQBKwv3rDuc9nU3RTMFR4eqmZxBJwY8A1iccx4ivJ5xhau4A4OjFM88huLZ6PSPWZXkCYaSKp4BIE8phjsQillOsNks2h8ksB1pHOgHm02QJ8vDZWWX4cueL8fM+2h3tGNr13BqeoREMOAZmDDFM0BVz6PVSxIBF74U5Zw9di/yUtlsRFE4n0migSojPxOLqXXCEPoqz4UhFKKKQk0Z8mX5dCJadRGw8J6UH2P1W6ERqXHzwpIYaakhC7Cz9/OUfWYU6Xh1fttosDScWRkMQjnAYgOBUeMiSzMlkKNK3sKzAABqkZraPxTMA3oPgRXyDb+nYzfdPuJ3dw+6E/s7R1GyJJ7AHsJIu42oms3sNcPkaId2MBiznDBIDOjz9FFimkWgLj2fhplHoSgA39mLu2bchZn6IULacjI+cDN3Bh2rOHsAeYpFCzYXuPhpYOmQql5VAtyyBeCOWpgWKoBr36LK8Sj4EhqG6IpXE44EI8vmyLs0nHMI7c52CDiC1P0Hm0MFJmY6hlkgL8EOmQJfcCNYOuk6Sox3fwMZXwYBR4Ad3VTFvq9/H46aj+KWqbegy9XFUDT3+ntRrCiO/R2XFQTa3609tjZj79WxcMh4CPlcKc5TTwV4IqhEathECqoE9zHVs/YArQhO2gccXEPzk658ccK+35mKRNV+48U0bRqcRRooVA+PAwHghk9vwNuNH2CaQQi9XIg1P5gzxruHYfNTAV50/l+hrMBfz/krWCxWvEd6Coy0VkgFPoePLEEWTL5vX5Rl8pqgEWmScx5pQCVUxYIBEyme9/bvhV6ih9k//NozB55Bk6UJHc4OFMoLY9vvnnE3fjTtR8iV5uKw6TBCJIRrqq7By/UvJ/xsqYCLrfcsxsxCJZZUanG434dtP1+CKoMML3/VhnKdDJNzFfi0vg9qKT+OeM6VxnNUJVmJiedkOWyTcxXY02bCb3b/Bj+f+XOIeWKUZpXGnjOD4UG0O9pjxDOPzcPC3IV4bM9jKFeWo1Zbi7dXvI252XPxVfsxLKrQQiMVxGxEijQSVBpkWDUrsd1XmO2ChCdJXAk0gUh6hezeTVd7P/nkk7h//+1grPgmACEED+18iOEFBiROswWoijoUCcHqt8IX8kEj0iBPloeHdz6Mf5yIXzUdC2avGWqhGtN102M3MADste7FRSUXMfbNmn0bfjXtLiwtWMrYnpZnEotFBzgrX6ODGL8j1sYxy4gA3F57O+6qGztZnjFocKbp8QxQZTSHD5zYBIASz1l8GU3A1o8xsJbqgLsboJHnU+KZx8cPs6Zg7fG1IITA7DNTnx1CgNYvYAx5oJMyVUHlynJ0ObvwVe9XOCv3rLE7YIkOcBuTvux0UlIxumIKADA2ArpqqlIcKo/nsFkoUtMS9D53H9478V6KHygxmoeIZ0fAASFXiNKsUnjC/dDI+BDzhybFAjlmGzgoVIvx2CdjJIiPB+FBIOiO99/OnwviMSO89kas518IVvZUWnpo7wC8Ftj8Nvzl8F/OSIVeFN5BL5499jP0O4dJjQ5nB7WwiIgRHpTBEx57IHLHljsYyc0prTb4cujFevDYPNxatxIeWyV63cNVBIc67ZiWP/bKtFLMRwgOZAlU8A+GY4rtQ512TNL9G5cBNrwPSLRAw9gJwJkgT5o3rsVIo9cYN0kT8jj49CcLMa9kWBWjlQlgdg8TjRXKCjTbaNWNyRWANoXvXFTxPF0/GSzfcMWHjC+LUzy3mj0oSUGi3PvFvbi++nrU6etQIKeKZ++gF9d8cg02tG1g7JtMoZAp+Fw2rpmVj1tGEPKZ4uaFJVhaFb/gWaAeVrpQf+szV+3MZrEh5omxs2fnaTl+t6ubBmOdgdDKBPi8cQBX1OXixS/bYPKaEB6U4pMjffjnofQWfgrlhQhFQjhhO4EGcwOqVFXQCcrAEfbh5DjyEtz+EKSC4coRuZAL1wjF85t7O/HExuSBze5ACJIkHs8AYuTryIUWXQLrJgA4MtAKEUsLFosFvUSPbEk2DpsOJz12t7sbR0xHYlY5Vr8VUr4UQu7E9e8akQYmL3MiLBFw4R0ce6GAKp4zX1yKwuQzwSLTUW/bIfQF7Miuvhy45i0AgE6so99Nng1UX5zymLaALU7RpRbkwRIwpfR3PpPRYG44c/ydo5DnUNXzSPTX0yo4kRKouAAooCHtaqGaPmMEUiB7KsQj5y+du2IEdRTuoHvsKomSxUBb4gD1kQGDbY428Ng8GH1GmMzHoBWpaNYMaNViv6cfRo8ROrEe7OveGXWgAsDeiZsm3TQsRooS6yORU0etP9LNuqm9ZngczWJRRXIilJ1DBSUjUXMJ8OeZwJF1SQ8fm/9a26h/dOmy2GsHBg5gum566u8IDPmq06qtRVw1vuCEsMu4Hwtn/phakgxZJubL8rGxbSM0Ig3WnVgHNdjIP/IBJDwJjF4jtnRugSPgQK+/F0XyotjhE/k8v1T/Et5rfg+P7n40ve+YBnrcPSgMBoBSOo9WSbNhnfMDoHQJUM+cv9v88f0HA11f08ymFJY//84w+8xodbROmOIZGBLLmSaAeFZJYpVv7qAbTdZjOOx5A3pt8oq+RAveVr+VUe3MYrEwO3s2gNT80WiMDJNLB4XyQkb4+LeFiTifKpEKx6zHIOQIExLPu3p3YUXxCsZ8YmfPTuzq3UXnOCPu/yhypbnY0rkFVaoqLMxdOOaiU6FaAhaLhdsWl+DhJXoYFELcd0EVNv50ES6qzUZ1thyNj16At26Zy6gITWS1AQwpnhMEJT66+1F81PJR3PZZRSqYwgcBwsOFxbQqtUBWEBP2RC2xRNzhcdEv5/0SvpAP5VnlYLPYqFZXo0BegJP2dlxZR58X80rUCIYjKRfHB7wTKzpIhqTM2K233goAWL16ddy//3ZE0zeTYcA7gI9aPsJLR19ibE9GyrJYLCgFNHAvR5oDFouFJflL4B50Z5ToG/1svViP2YbZeGjnQ7j646tBCMFJz8n4AUH5uTh38vVxlhfVqmoGyTUm2BxApMC7ze/DF/LhhO0EKpWVY76lQF6QshxDL9YP+zxnYrUBAFf8HfjkbqD+fbjDbigI6ECGl2ISI1LGwnC6WSFMC4Yh4Apg9VspGe3qAmzt8Ky5FOFICLJRNyifw0dJVgl0Yl3qm1eeTVOqk6DLxAcLLKZf9kAjoK+JK4/PVYrQ5/Dj2UPPYu3xtWN/bhI0G10o10mpT51ICxlfBjFPjGU1IyagQjnYQReevHoaNjcZsa99AsvYvBY6WGaP6pLYbOwTL4Cxrwt/8F0KeXYpVYW4jYDHFFOujTTfP9Mg5UtRrChFf3CYrO9wdqBIXoQWkwcGiS5uYj4Sg+FBWP1Whv1NOlYb0RVYMZ8LvUSP9Q1NuPz5Xeh3+LHzpAl1hWMrnjlsFjg8D/gsOf646Th+8d4RBEMRmNwB6KTfghr0yDqgefPEHjMcAhr/SW1cGj6cMLuNifR4Bqi38UhVzMi0Y4BW3cTUVmMonr2DXqxvXY92Bx2UlemkDFWnlCeN83hOFSwYjoRRb6mP+XYXyArQ6mjFQzsfgj1gx67eXYz9/7T/T1jfuj7p8TLBY5dPGZfSORUKVWJ0WKkqtdXkSRms+K/GipIV+KT19IgAetw9cI5WGqaBD5o/mLDznAxamQA9dh9+fl4lum0+NJm64fVJ8NtLJ+PFnemVNLJZbKysXIm3j72NT9s+xQz9DIhJAdiCXrSMQ/HsCoQY4YJyETNcsNnoYvhG7jhhYpDG3jE8ngFKPLNZgEEx/CzWywQJfZ5PWDqh4g+PPRblLYqpBeO+d9CFQDiAudlzY4sYfe6+CZ94aEQahkIJoOoiX4qqqYysNhLA5DXBGrAC/OFj9Hn6YJAXULUlKPHMyBNJAZs/PjxbK1IhGHFmHBB8JoHFYqFSNfa4/VuHIoHPc8/+YZ/k69ZS0QuGFM9RMiJvJoTWER677V8BhfMYh0mpeFaXU4FDAoy0Rmx3tqNWWwuT1wSzvQ3aEV6sUeIzUVUTgBjxzEAixXP+bBrg17U3/crP8eLiPwHXvEGDB5OMjWLhgvXvDVl2DC+6HbcdT38BQ1NJwyHNzSj2OsHn8JEvy4dWrB1SeR8EImHkyfJwyHQI11Zdi4PGg6gNhoD6f6BAVoAOZwce3/M4Pu/4HD2+Hobi2SAxYF//PsYi6oGBA3hwzoPodHZOmP9zj6sHOY4+qpIH/X2OW4/jNX0BwrufBf6+JEai2wLx/QcDxiZm4ON/IN5qegt/3PfHCSWea7W1aDA3nHLla06WECZXAIFQGCdsJ6AVFELPXoyuYGKhVY+7Bxd9cFEcSWr1WZPabI4M50y1wB8hEfS6e5ErS/++X5i3EF90Ja7WOJ0w+iaAeBaqECZh1Gpr435TR8CBzR2bcVXFVXAGnDHF8IB3AA2WBrQ72xmK5yhypbkweo2oUdWgSlWFdmc7/KHkGRkAUKaTYapheJ5RaZDFBHiiBCKBXndvRornfk9/zFJkJDhsFjSGI9BhcWz+lyvNxYB3AIPhQRyzHkOVmmkVqxKq8MbyNxihmyp+NjyRAcwrodWd0wuywOey06pkNYj/hcTzggULsGDBAsyZMwdTp07F+eefj2nTpmHZsmXJ3kISDVIAACAASURBVPJfg1QrVsesx1CtqsaHLR8yyIhkimeADmZ2dO/AZA0tC7y99nZ8f/L34QhmFtrU7+2HXqLHdVXXYdNVmzDgHcC+/n3gsXhpdwqlWaXocHakXcpLhAo8eeIt7OjegZP2k2Mn9qYJg8QwbBWSidUGABQvApY9DDR9BFfIBXnARxUMaSBastc56Ea+YwAGsQH9XlrSa/Pb4O07BCOPD11oECxJfMn2FM2UuPDIhJBl0/TqJGjo8UDB1yZQPE+iZNEI4lkj5eOYuQWfd3w+LnsWXzAMozOAApUYZp85ZpVSpCjAqvkjJn4CGRBwQiHi4Z7zKvD4p00TV1bvMVMV6ggEQmF4gyHcZ1qOB6S/RYDFh1hXQq0S5DkAR4CjA9+Ay+LGgtbOVCwrWIQArx6BEA3t7HJ1oUBegFaTG4UKw5ilUdEH8EjiOZXVRpWqCkvzhysZzi4rh1DkRm6WCGc/uR2XT89DlUGe9P0AXcln8S3Y2RTBW193UlW9wwedTAhuGj5np4z9rwLNn03sMXu+AWQGoHI5XYiaILuNqI9YphjwDKTVL6slAobHs0FCA1hD4Qi+brNiUk7ic/lV71d44MsHsK1rG4oVxSjVSdAyQtUp48vgGmRabTQP0OqHZLAFbJDz5eCx6YQzX5aPT9s+RSAcwNNLn47LF2hxtKDB0pCyjWnjs4eBxni1wKmgSCNBu5kqXdrSUHz/q3Fe4XnY1bsrtmhg89vGDgTOAD2unrhrIh0cMh3Crp5dqXc8BWhlQhjkQuRkibCoXIPtJ1tBQjIsq9ah2+ZLfYAhXFVxFT5p/QRf932D5QXXIuCXgc0J4Zhx7DLzRKCK5+HJiFzItNpoHnCj3+mPkc3PbGnGntZhxY47kNpqI1shAm+Ep75hKDNgtOVVp7MbhhFeg4vzFscRzxESwSHjoVh56KK8RbHFoon0d46iRl2D90+8zygtVYh4cAVSEc/eU1pkMvvMcZUWo0tiY4rnNGHz2+IIBZ1EDXA9KNL8+9pPrb1oLUNFdUZAnkNFJ1GEB4GBeiB7WtyuaqE6RuYgexqEQ/kH6DsChPyAnqn69Qx6xhw/QayiNh+h+DmQQqCAI+CAM+iEP+RHjbqGClM8vchVDZMCUeK5x92TeDFHXUYVvwE39VYOuGllpmYU8awuBW74EFjyALUEPN0oXkLbnmRsFAsXPLkZqLyQ8ZrZZ4ZOkib5pK8Bdj8L/H0pWIffxoWqqbHsCYiU9Pzvegb5Q/3ZFeVXQMKToNZuBKxtKBLpsKdvD4w+IzZ3bIYn7GH8zpeWXoo2Rxtu2nATIiQCs88Me8COqdqpUAgUSb1e00WHswODkUH02luQ63PHfJlVQhWeOfAMXu7bgXs0Skpwm2l1mt1vTyy62vMXen2bjtO8of9gHDAewAHjAfS6eyeMeJbxZShTlp2y3QaXw4ZBIUS3zYdj1mPwuQ1YWFSNPn/iccGm9k2w+W14Yt8TjO0WvyUulD2KCmUFDhoPotnWjMVrF8eFZI6E0WuEQqDIqG9emr8U27u2j2thpd5cP24LN1rZcerEMwDU6eviOLa1x9diSf4S5MnyIBfIYfPbMBihoqx6cz26nF1JiWeAzomFXCGKFcU4Zj02YeNlYMjjOYHiuVhejE5nZ9xnmX3mhJVoFp8FdnIMLW2lsW08DuXuej29aLI2oVoVvzCVI81hXCMnerhgc7wAm55LIY+Dv90wAzXZY8/5E2UOnQ4kJZ537tyJnTt3YuHChdi0aVPs39Sp//mm96mQDvE8P2c+bplyC+7ceic2d2zGb3f/FgeNB1GRlZx43t61PZZ+CgwPcDLBgIcq6DhsDjQiDeYY5uDFoy+iVFqa+s1DEHKFyJflp22K7hBI4Q0HsKZxDVRC1dgDujShl+jR7+0HQgFq45HuYCaKooVA25dwD7qh8Fhiq9FrdrfjT5uTB0PyOXyIuWK4wj7ozCdhkOhjpRJFiiJ09u6DqfpCaCMsQBz/YPlp3U9xW+1tqb+fLBtwJp/kHu1xYI5hHrN0xDikeB6lUtTKBNhn3oxLSi/J+HoBgBaTG0VqCbgcNsw+MzRDPssjSzwAAAJ5zHPvsmm5CIYieH13B+AaoAqJU4HHRH2vR2DB77fhxpe+RllRIR66/gKsmpkPlrKIXg/KQkCiwVHjIczPnc/8nmcgFucvBl92LFbOHw2MaDV7UKnJg9Gb3HbF6KOvRT3Gg+EgwpHwmAEA03TTcOOkYe+9W8+aATbPgSeumoqHVtTgvvNTq5sGvAPIEkrw8UEHrqjLRZ/dj1azB/mqb2GCOugHuvdRBdBEwj1APRhZLFp+2/3NhBw2R5qDfk9/xoOZdH3uNKOsNiQ8CTgsDrY2d0AnF6BMl1j9fshIlUKL8xajWFGMYo0EXTYfBsN0UDq6L4lECJr6nEmJ7ETfeZJmEu6ffT+eWfYMqlXVcAadMTLHHXTD6DWi0TKB1jy9h2hfmArWVuqLnwZKtVK0DhHyLWe41QZAxwazDLOwuXMzPjz5Ic5Zdw5WfLBiQlRcPe4euIKujI9l8ppShxKfIrRSAWrz6WLswgoNvu5qh0Gig1rCh38wnHbWgUakwU/rfoq5krvwwvY+9DsDyOLmoiVBWWQquAOjrDZEXDiGiOfBcAQdVi/mlahxqIsuCreaPXCNUER7UlhtTMqR482bmd6SerkQ7RYvzvr9VrSP8I82+npRnDXsBzlJPQkDngEGAdvmaMMtn92CNkcb8qR5qFHX4LiVEnWng3j+7qTvAgBeOPpCbFu+Sow+19h5MT12/7itNqJVQqPH6aPVY1qxdsxn72hY/dY44kgjlQARHjTy05B78d8MeS6TeB5ooKF0gvj5hUqogsVnoUKI7Nph4vnQm0DttXGVdCkVz2wOzRtJEGCtFFCP43YHVdjpxDoYvUY0D9pRnjc/tl+UeD5iOhITFDEg01NBzO5ngZcvADbcRz8vURZNbh0w59Z4a4zTATab+jYffCPhy1nCLDgCdkT6jlDv7BEYKVxJicrlwD3HaeBgyIc7J9+CmybdNPz6Va8Axz5Ffv8x5EpzoRFpcEvhRVjE1wDl56HA58bHLR+jVluLXb27kC3MZtgbTtJMwt/O/Ru4bC6+6vkKBwYOYJpuGtgsNooVxWhztGX800Rx1HQUKz9eiY1tG9FtPIq8wsX0mgG9FkVcET66/GM08LnoyJkSG4fYAjZmFSsA+J3AxvtpGCFf/B9tsxEMB9FoaYRKSC0VJsrjGQDmZs/Fnr49p3ycQjUNmm6yHIPRrMIFlVPGJJ5XL1yNAwMHYs9QIN5qYyTOLzofWzu3Yt2JdViStwSP7XkMLx59EYOR+Odhp7MzbX/nKIoVxRDzxGiyNKXeeRQe+PIBfNmT2GIoFYxeY/r3fhKohWqIuCJUqaoYimdfyIc3m97E9yZ9DwAdv1n8Fpi9tL9xBp2Q8+UJ+/QoIRxVCk/RTMH61vVYtm4Z7H573P6ZwhV0YTAyGH9fg4YrK4VKhiiJEAKL3wKj1xg3PtnSuQULchei08LMyolyMVFRayrsOGGGkm9ghL4vrdSByxnbf7vf2/+vtdqIoru7G9nZdCCq1+vR15e5IuQ/DcmIZ1/IB1fQhePW46hSVeHGmhuxIGcBXql/BYXyQjw450HkyRJ79SgFShh9xuF0Y4yPeDZ6jYzOfG7OXOzu240ySdkY74pHpaoybbuNHr4AKq4Yh02HU9pspAu9WE8Vz85eQGqIt2BIhax8QCBFyHwAiqAPyJ6Gtfs6sXrDMXzdPrbfqEakQa4sD2xCYOBnocHSALVQTW9+SxOMWfnQFS2m5WCjELWoSAlZdtIQD5snCF8wjD8s+W0spRWEDJVhxVttaKQCWANmVKoqEY6E4Qulr/wCgJNGN8r0dDBv8pmgEQ4Rz0PerTEIFXSQBBpw9JfrZ+D57SfRtO1tYMcfgcjwxIsQgj5HBt/Da2EECwZCYdi9QRRrJLh9SSnK9TI8fFENJft5EiCrEBGJBkdtx7GieMUZbbUBAJXKSvC5HLx/nK5udzo7qUWByY1qvQG+kC9p+Y/Za0axojhG6kRtNjIJKVIL1bAH7OBzCa6bU5BWMvNJ+0lUqMqw6a5F+NVFk6BXCLCnxXJK5c9po/cAVdhPNPHsdwDCoQFCznTqnTgBEHAEGYd6EELSVjxrpHyG4hmgfeR7hxtw2bT4ahDvoBeEEBwyHsK5hefiqaVPQcQVQcDlIEchRIeFklUj/eIBoMvmhVzEQ5Y4ebiEyWui5bBDEHFFuL76enDZXLBZbEzXTsd+Iy0ja3W0okJZAaPXGBdiOG44ugFHcs+9GLY+Bmx7PK1D5mSJYPUG4Q2GqOJbm9zG5kxB1G7j7WNv4+mlT4PNYse87m1+27g8oMORMPo8feCxefAMJg/ES4Sod+NEKklGY3GlFlfNoBOxs8o0GIQdxUoDWCwWcrJE6LGn/8y5vvp68ML5aOp3ot/pR6G8GF7Sy7DJSAfuUVYbWulwdUKHxYMchRBzS9Q42GmH3RuE1ROEc4QHdKpwQRaLFecfrJMJ8N7+bphcAXx5cniC5hgcQI1muNycw+agTl+HbwaGF9isfiv8YT/Wt65HniwPJYoStDvbEYqEaKnlBE88uGwubqi5gaFGK9aI0etk/s4vHn2Rof6yeYJQScYXcmPxWyDiioZVsKD97eiSWL1YH1vYTQVCSCwcbCSUYh5YESlY3MxtWv6HMaAspouHUfTspwRsAoh5YnDYHDouVpWCE7ADbhNwdB0w7bq4/T1Bz9gezwDNfXHH27BkCbJgD9hjdmkakQbNtmYESBiGggWx/fRiPUw+E/b178MMffxcAQCw4G5g+2o6Fmn4gBLr7OSLUN8ays4BOvcmfInH5kHCEcCZW0sDCUfA7DPHhVEnBZtDq8805cDdTWDlz2SOaQ2Tgdm3YLbbge9P/j4A4Ac8PQy5c4DKC1FkaceAdwDnFJyDMmUZcoTxikMWi4Uba27Ei0dfxNaurajT0eunRFEyLuK5z92HmzbchB9+/kPM0M/A4Z7d6AnakXPWcDDphcUX4sklT0IhUKBWW4t6VigWJB/zeHb1U4UzQH29AeCrp//j1c715noUK4qxKG8RCEj610oamJs9F7t7d5/ycQpUNO/jsLEROeISVKlL0O/vj6vubXW0YsAzgLNyz8LivMX4uv/r2GsWvyUp8ayX6FGuLMfa42vxk7qf4K0Vb2Fzx2a8f+L9uH3XNK3Bwrw0KqhHYUHuAuztT3z/JoMv5EOnq3NchDUwMVYbWrEWxYriuEDiD09+iKnaqShTUh5LLVTD7DPD6KMZOTXqGhQq4tXOABUT/n7h72MivsmayVh7fC1cQRdaHC3j+p7BcBDdrm48c+AZXP3x1ZiimZJ0Pl6iYNpteENUfFanq8NhI1P13GBpwGzDDAi4HNi9w+OjAnkB6i31OGk7mVYI8JfNZpQpizIW5EWFq6cbKdm80tJS3HvvvVizZg3uuecezJiR5AH6X4RkxPPzh57HHZvvQJO1CZWqSrBYLNw98268ueJN3DjpRlxUclHSi1MpVELKk6Isa5ggVvDHoXj2MqXyc7Np+EapJH3FMwBUKasYK3hjoZfDxnSBDmVZZal94v62iBIHKWCQGDAwcJgSB5n4O49E0UKQnq1QFNHV6K/bbLhsei7c/rFVUVqRlq4yaithiABHzUehFWtRKC9Ep6sTRoEIOmUZwDkFn1uxmobpDQ6RjbYOYGjC3mH1okAtBmvbY3Q7QK0o2BxArIpTKWqkAjiDVqiF6iE1QmbXTKvZg1KNJFaOFiWVoh5qMQjkQGCYPCpQi7H8rJN4pWMdEHDC1H409treNiuueH5X+lYcHhPDasM6NOn8w8pazCgcoTBisajaOasQPWI5xGwe6vR16HJ2UuLb2gr9gSczav+3ARaLhfP1P8E/2p+C2WdGp4uuZLeaPCjVyqAVaZOSlkafEXW6Olh8FngHvejz9DGIv3QQrYBId5INUIV1WVYZJAIu+Fw2itQSfNlsRv63QTy3fwVMuYoO0EOn5tvGgN8BCIfUvDnTqY9gFB4z8NeFwAe300lrhsiV5mYUMGgP2CHkCtNaqNJIBbC4g2g1uRGJ0HvKIDFgd0cLLprK7B8D4QBWrV+F5w49h2Z7c5zaqlQrxUkjJRXlfDnDVqGh14nqUeVY3kEvbtpwEw7YKUmfStlQp6+LZRO0OlpRrixHpbJy3ANaBiIRuhg5OnQqEVx9wOF3aAlzCnDYLBSqJNjTakEgFP52VP2niMV5i9FgboDNb8P8nPmYrJmMo2baB7/X/B5u33w7PmjOLEDT6DVCKVBCKVRmvFBg9BrBZrHR7c48ZDNdLK7Q4twaOr7RyYSQij2YpKcJ3blZIvRkYLcBAE7/II73u9Bj86FYXgyFwpaxz7PLH4J0BHGslgrg8A1iMBxB84Ab5XoZphdk4WCnHS0mT+w9UXgCYcb704FeLoQrEMK5NXrsbqETtGAogkG2GVP0xYx9ZxlmYV//vtjfUTJ2Z89O5MnyIOaJoRPr0OnsPC2KZ2CUBy+AIrUEPaOI5z29exhjTrsviCwxD+OB0WtEkbwIvpAvVjps8Vsg5AoZqiitKH3Fsy/kAwusuP5aKeZDwJLHhYn/D6cIXTUVW0TR/mVCsUcUsYBBNhuBrDLgnz+i+6uK4/ZNZVUGAJDqEwaAZwkp8dzt7kaeLA86sQ4HjQdQHmGDJRx+dvI5fCgECph95uSCnJxpwKXPAZc+S1XGo/2d/1XQVlHSfzCxGEJJWLDkMRcBoiHs4yITkym5NRXIsXTg6sqr6Zjs5GYgbxZQdjYKeql91zTdNCzJW4IiUQG1KhmFC4ougH6ocnWhohwwHaeKZ2fmxPPmzs3QiXX452X/xA+n/hC72jdhkMOFUjNMGJcry2MCsimaKTgacQO+IcVz1CP+/VtoxghAbTi0VYDp2H+8v/MB4wHU6eowyzALKqEKPM74+vdEmKadhnZne0bBfYlQqBajzexCl7sds3MnQSFQgMPiMBYx1x5bixs+vQE3T7kZXDYXswyzmMSzzwK1MLHVBgBcXHIxpmmnoVhRjBxpDs4uOBt9HqYQ7YuuL9DuaMeNNTcmOUpy5EhzMrKQAoBWeysiJIIm6ziJ5wnw7J6um47nzn4upmgGgMHIIF6tfxU/mPyD2H5RYjo6F5mknpTQZiOK5SXLY9UQ87Ln4dqqa7G8eHmsijhTLH9/OW7ccCNsARueXvo0XjjvhaT7jhbwRfvIWl1tXCBmo6URNeoaZCuEDBFFgawAbzW9hXk588Z8bhFC0Gv3IRCKoEpTnLEg73QIDxIhJfH829/+FitWrEAgEMDy5ctx3333nfYvdaZjNPH8aeunCEVC2NG9A73uXlj9VhTICjI6plKoRK22llEqJBfI4Qw6Myp5Ha2gy5HmYFXlKpRL40MNx0KFqiJtxXMvG8hh8XHn9DtxXuF5yXcMeqnnmi31zaAX69Hv7gX6jyRVOaRE5YWwitVQFFOvW5s3iHylGK4U5bhqkZqmyGoroQ8GcMxyDBqRBvmSHHQGHTCxkVEHa3YHYPOM8k1is+nANqp6fvcGoGUb0HcY2o0/xGyZBdjxB+DkkALI0RUrwRNwBIiQSGxCpZUJ4Is4oBapY2WAmaDX7gNb2INV61fB7B0eOMYpnoc8niMkgks+vATuoBt+VjsI24xO2TQ88dKbsQC0+h4H+hx+HB9Ik7zwmBlWGxZ3ECpJksGoqgRQl8IokCCbK4FOrIPLb4F366PAnr8iwj0zvRbPL50P0eA0vFL/CoxeI7J4enTbfCjVSaAVa5MOFExeE7Il2SiUF6LV0Yqj5qOYokmSVD4GYlUEaeKk/SRKs4YXrIrUEjT2Ob8dUq7jK6B4MQ3gsY6/JDIOfmcskAi6anpfBVzUy/GNK2gyOYsNfPZQxofOlmRTe6A00e/pT7sfEfI4mFeqxiXPfoUvmul1ohJqEWLZoZcz75MXj74IrViL1xpeQ6miNM4frkwnjfk8i7giBMPBmP9qY2+8zcYjux5Bu7MdRxw0DXp0Vc1o1OnrYsEZrY5WlCpKUaOumRi7Da8ZCAeYJdjJ4Oqj18+R9AJXS7QSvH+gB7X5WRlVE/yrIOQKsbxkOa6pugYcNodOcoeI5509O/HzmT/H6q9XgxACR8AB76A35TG73d3IleVCzpenDL4ZiVAkBEfAgTp9HU7aTq/dRhSEEAglRqycSsPG8pQidGegeAYAh28QgVAEh7rsqNaUQyAyob43s2BFTyAE2QjFM4fNglJMKxSajW6U66SoK1CiodeBo920tDPOaiND4jlPKYJGKsCvLqrB7hYLIhGCTqsbbJ4dBQpmVd1o4tnmt6FEUYIIidBxDoDyrHI025tPH/E80oMX1GrD4g0zPKpbHC0xlZN/MIxIBBDxxqf+NHlN0Iv1UAmGx+qj/Z0BOtazB+wM/+lkSGSzAQDzStWo0eecMuHxP4yCtpISieFB4JtX6Nyh5rKku0fzWQDAr6wE2r4ALvw9Y5/ByCD2D+ynHs+pFM8yfVLFsy1gQ6+7FznSHGhEGgQjgyjnxxNNBrEB03XTwRlLxTz9O3RMcs4jwIozRDTBE9JxtikxCaUPBjCgYYqZoko+8USOvzXllAD3WIBnZwCREFBxASBSokA7GXkCFarV1bhz+p34DtEALyyLs9ficXh4YtETePfid1Gx92XgrwtR3Nc4LsXz3r69OLvwbGjFWtR0fIP+SBC58qKk44XJmsloCFpiimd7YMjj2dJC80YAwHyCXtfyvP94xfP+gf2YoZ+BednzcPeMuyf02DwOD0vzl+Ljlo9P6Ti1+XJ81PMURJEyzC2mz4tsYXZMPbqvfx/+euSveGfFO/hOzXcAADMNM7F/YD+cQSe6Xd2w+q1JPZ4B6lf+13P/GvtbKVQynh/+kB+rv16NB2Y/AD4n86ofjUiTUfUlQDPI6nR14xaIdLu6EwbsZQI2iw2NSBNbqI5WbSoECoYbQPR1o9cIrViLmybdhB9O/WFan6GX6PHgnAdRrixP2052JAbDg7D4Ldi8cjN+Pe/XqFRVMni70ciT5jEESVHiuVhRzOBXguEg2h3tKFeWIzdLhN6RxLO8APaAHZeUXpL0c/72RQseeP8oDnbaUVeQhUJ5IZO/SQPpWj+eKlISz16vFwcPHkRrayvC4TA6Os7skvZvAwqBAp6gB4ORQZi8Jvziy1/g1YZXYQ/Y8fTSp3FJ6SVjDzQSYHHeYoYnK0BLmkRcUUYlrwPe+NLth+c+DBEnM7KoLKss7ZuyhwSRS9hYVrAMZdI8OkhIBMtJACSpxcRI6CV6DEQCdCB2weoMvvkIVK1An246FEOl9TZvEAUqMUNtlAjTddNpaZymEgaPDcFIEFqRFtUcCQ6LRBjwWzIinv/2RQue2JSAxB8ZMGjvBAaOAm07kNuzET8e+DUdiPQMKTIdXZREAVXPjrTb0EgFCBIHVTwPlQFmgl67DyyeFcesx7C3f2+MeM6X5aPT1TmsWhbKAb8TfZ4+tDnacLx9K1qMh+Di8fGabSrm8NvQPuRh3NTnQpaYh23H0nz4jfJ4tniCUCcrs73sL0D1JbAJRFCCAzaLjbww0HngJeDIWtjKrkz8vn8xpuQqYO2bjXePvwudWIemPi+qsmUQcDnUJzCJGjn6cK1QVqDR0oh6c31iz8AU0Iv1GPCmTzxHFc9RRMu9vxWrjYEGqgZSl06s3YbfMUw8c3g0rKfvMHDgNVqFcM6jwLmPAsc30n0zgF6SGbH/RfcXyUtwE+C178/GuTV6WIe8nqUcNcRiF2PSQwjBaw2v4fEFj+NH036EZQXxYcClOilODqk6bd5BcCDEk58fQSRC0NDriCOet3dvxyPzHkGzm5aERq/HZKhR1aDb1Q1n0IlWeytKFCWoUlWh2Z7cWz9tOLrohNjRTe2HAGDLb+jC1UgQQj30594GtGxN69AlWgk+bxxAbV68V9uZigfnPBjzvYsSz66gC02WJlxdeTW4LC6cQSeeO/Qc3mx6M+XxouScnC/PSPFs9VuhEChQqaw87T7PUVj8FrBYgGEo/2FcimdfCHIhF4FQBNMMFQiy+3GwI7OFW+rxzCSOdXIBjM4h4lkvhUTARV2hEmv2dKBEK4F7xOK3O4XHcyKUaKX44t4lyFeJoRTz0djnxMGeDnAhiltoqlRWwuQ1xSa2Nr8NS/KXQMQVxfwjy5Rl+LL7S3S7upOWrJ4KVCIV7H57TETB47ChkXDQZaPjBWfQCbPPHCOeHb5BKMS8cS8AmXzUDmgkGRklCkeCy+ZCKVAyynqTIVYmPwoyIQ8lKj2sfis6nZ0JvTr/h3GAJwLk2fT5/PmvgOvXjel/qxaqY9e4o+B84JI/02fFCBwcOIi7tt0FV9A1tsczkFzxLMiCw+9An7sPOZKcWPVPuaIkbt9saTbq9GkKZ3giaj1xpsAwBeg/Gr89EkaOz4VePnPBO0qoTOiiLV9CKyEPvUmVzt95jy4IABBUXogN4loIOAKwWCwI3F3AoBfY/js6B41EgCPrgA330/FA0AOc3ALc9BGKv3kjY+I5FAnhwMABzDbMpl9tz19QrShFbhL7TACoVlWj2WfC4AirjSyOiC6cR23ezM2AtgK4+Gnqe/0finAkjMPGw6jT10HME+PSsksn/DNWVa7CuhPrTinrYof5VYjFXvQeX4WZQ9W2BqEhph79ze7f4NH5jyJfPuy9rBFpoBFpcPXHV+Nn2382psczQOfwI5/TscDOIbxc/zJq1DWYnzs/0dtTYrRVRTpotjdjUd4iBCPBjNXSgXAAVr91wtSyAo4AQq4QzqATnc5OVCiZ2WgjFc96sR4akSbjzy5RlIxL8WzxW6ASqMYkm0ciX5bPqAKM9pOj5+PNtmYUyAsg5AqRnSVEn2O42qRUUQqd7FdTXwAAIABJREFUSJf0ejje78LfdrTikyN92HnSjOkFShTICzJSPEdIhP6e/8pwwSgefPBB5Ofno729HRqNBg89lLkSDAAikQh+9atfYdWqVbjhhhviCOytW7fiyiuvxKpVq/Duu++O+Z6Ojg5ce+21uO666/DrX/8akcj4O5nxgM1iQy6QwxFw4LDpMFRCFf588M84K+csTNFOwcNzH874mNXqaszPib+oFAJFUiIxHCF48ctWfHiwB5EIQSgSgtlnhkF86je/RqShitYkvrMj0RvxI3sorApH3gE++dnwi03rgQ9uo8EJ5hN0WwIVAQDA1g6c+AwAIOPJQEDgShAkkgk8YQ/kAkqk2L2DKFCJU1ptXFN1Dc4rOg/QViLbQZOPtWItakIRuDhcHDIeylDxHMSnR/vjEughzwZcvbQU3GcDBhqB/qPYpbwMimA/sPwJ6msHAPYuYISSScKTwBOkCxIaKR9hlgtqkTpWBpgJeu0+sLlucFgcWP3W2EBaIVCAy+YOr8QKswC/PTZga/xyNVpd3XBJFLjuiiswh9+GTiv9To19TnxvfjG2HU/T2sFrGWW1EYBamoR4FsoBDhdWLh/KCABCUBbw4/XCKfCUn4vQBCUlTzSyxHwo+bkokVehQFaAA5021BXQgY1WRBXPgXC8rYTJZ4JWpMXSgqX4rP0z1Jvrx6d4zoAYJYSgxd7CUDwXayjhfNoVzz4bMOijCzPqstNHPANA7kwajPnlk8DZv6JWLhINDf2pfy+jQ0fDhNIBIQQft3yMS0szG3wrRLxYcJmApYJAwCQHHQEHuGwuDBIDvjv5u7hl6i1xxyjTSXFiqBLhnncPgQ0R9nR04+Jnd2Jfuw2Tc4d/n1AkhGA4iDnZc9Dp60QwHEzp5cbj8DBZMxn7+/fjuO04SrJKkC3NjislHBcc3YCuBmBz6XUCAPtejA9C8jvoPtpqas2RBko0UkpA5v/7EM9sFjs20a9SVaHV3opN7ZswXTcdIq4IGrEGJq8Jfe6+tCbazbZm5MnyIOPL4Aykr/yNEn1lWWUTs8CQBo5bj6NCWRFrf64yM49ngFptzC5WQ8TjoEJVAH/Egf1d6d3D4QjBYDgCt5/p8QwAepkQRlcArSY3SjR0DLO0UocWkwfT8rKYVhspPJ6TIfqe2cUq7O+wYXPXRuQKpsftx2FzaDDykGLL6rdCL9bjnYveQbGC2hCUK8vxz5Z/YmXFSsj5Yyefjwc8Ng8SvoQxNsmR8WLBiK32VvDZ/Nhk2e4dRJZo/GXY0cWxaOgcQBdVogrvkdCJdWlNtG2BxMQzMFwF+aMtP8KbjW/iiOkI/nLoL+P+/v/DEHQ19PmcP4cuQo+BkXYufs1kYOrVcfs0WZtgD9AxbGqP58SKZ6VACVvAhh53D3KkOZDwJBCBhXL9tLh9fzbjZ1hVuWrszzlTMZp4DoeomtjahmyOCL1+psDI5DVNqGdvDJpy+owvXszcXnEhcGJTbAGa7+qmntmt24A/1wGr86h/dssWOv88sZGS1wVzoZXo4R/0ZFTV02BpQLY0mxKK9i7AZ0Nt7rykuU0A9R7PFxtw3E/7NVvABlXAS4MzBxqomt/cDGgqgPJzY6T6fyJO2E5AI9aMScieKmq1teBz+IwKn0zwdd/X2Ni2EX857/+wfHIBcrLoXCdbmI1OVyf6Pf1wBBxYmBvvu3x+0fk4t/BcWHwWtNhbMmrnyCp6Z9CJN5vexL0z7x1XG4B4a6t00GxrRrmyHNWq6oztNnrdvTBIDOCyT8F+dBSiPs6drk4UyJkOAtEF5dF5M5mgNKt0XIpni98ClSj9c5sni1c8q4XqWChtFA2Whph/c84oxXO+PB8brtwAHjvxmGj1hibcdU45phcq8Y/9XagrUNJMMlf6Hs8mrwkyvgwCzukPsE1JPNvtdlx11VXgcrmoq6tL37N1FDZv3oxgMIi1a9finnvuwe9+97vYa4ODg1i9ejVefvllrFmzBmvXroXJZEr6ntWrV+Ouu+7CW2+9BUIItmzZMq7vdCqIDmgPmw7juqrrUK2qxtkFZ0/458j58qQTwJ+8fRCfNQzg7zta8fTmEzB6jVCL1BPim8RmsaGX6NMiC3oGXcgdHLKSsLZSAjWKw2/TVegdfwBMx2kwnCvJxK5lG7DrGQB0RVAfCmOANb7rLQp3yA2FgBIpNm8QOVlCBEJhhMJpLFZoKqA3ngQLLKiFarB9Nizma2KT7HRhHQoL/LJ51ORGlk1Vec4egMWhA5H+o/iQfTa+WfUNUH4eYO+gVgCObkbatYwvi3mzsjl+AGwgwqdlgBlYbUQiBL0OP8IsFy4ovoA2e0TIX4FshN2GRAu4jbFB+5awDSEOFw6RAqVT5sEQ7ES3yY5gKII2sxs3zS/EiQEXHvmogaHwGvUFgN5DVPEsHm21MXaJkY3Nhio0CHjMeNgVhE9XiadyJl6tNZGYkqvAzKwrsSR/CQ522jG9gJJcWrEWL9e/jPP+cR78IT9sfhsGh8JHTD4TdGIdFuUtQpO1CX2ePgYhnC4MYkPaVhCfdXwGg8TAICEK1RLwOCzoZcKMPzshur+hi1KjnynmZjrZYLEmXvEccFKv8iiW/ALIn03LXXNGkDZjpLonQya/7yHTIXDYHEzOqgDWfoeqcdKAfATxzAlngcVjqrLTKZWqyZajzeyBwzeIb9ptyJOr8Mhlpbj3/Ep8cMf82GAbANxB6oUp5olhEBpw3HocJq8p5cJbnb4OT+x7AjmSHBTJi+hvkyYpPyYc3XQBTp5L+81QgJLMB15jhJvC1UcX9uQ5aVXYAFTxDABT8xQp9jwzIeQKMS9nHp49+CwuLL4QAKAT6WDymTDgHUipfjhoPIj1retxaemlGVttRC2aZhlmYU/fnowDbseDE7YTDCVMbpYYPbbUdiIj4fANYm6JCtkKIbgcLgrlhbAGe2KWUWPh9d3teOiDo5Q45scrngecfrSbPbFKkaVV9J6ZmqdgWG14A+FxEc9RTMpVoL7HhoP2T7Ek+/KE++RJ82LPcavfCpVIhRJFSYy0r1ZVQyFQ4LuTvjvu75EK0YlkFDlyHtqixLOjFbW62hHE8/j9nQE6udOKqOJ5LKsNgD570/F5tvqtUAkSTzhVQhW6XF3odffi1YZX8cuvfonXGl9Ly8LjfxgDumqg6WOgakXKXUeq2/dY9+AXO34RO/fHrcfhDDpx3HocLLBw2HR43OGCCqECNo8RA54+ZEuzwWKxcJs3guric+L2zZflQ8Y/84NqE2I08dz0T2DtDYCpCTmS+IVks3+c/s6poK2k86DiRcztmjIAJJYZxHd3AQVzgB/vA+7vAH5WD9yxG7j8b8Cn9wAbHwQmXwEAYFWcjwK2KGUpOiEEf9j3B4QjYRwYOIBZhln0hbYvgOLFuHXqD3HLlPjF/ZGYoZ2GPWH6LLX77cjy2uhvm5VPf19bG6DKfDz/74aov/PpBIvFwnmF540rWBkA/tH8D9w27TZMyc7B89cPVyPmifLQYG7AgYEDqNPXJVT1/2jaj3DPzHtwftH5CIQD4yae3zvxHhblLUK2dPyWV2NlBiVDs60ZFcoKVKurM7bb6HZ1J1zUPRVoRBoYvUZ0ubrirGvVQjUGPAOn5CttkBjgCroyzjKx+qxj+nePRq40Fz3unpgK3+KzQCPSQCfSwewzx7Y3WZtQraIe7zkKEXodTNFnMsuVLqsXh7vsuHpmPi6emo0IAWrzFTBIDHAEHGmPxff27z3t92cUaWnFW1qoHL2/vx9sdnry8tHYv38/Fi6kq0TTpk1DfX094/gFBQVQKBTg8/mYMWMGvvnmm6TvaWhowOzZtNxl0aJF2LVr17i+06kgWtZ1yHgI03XTsWb5GpxdOPHEs0KQOGCw3+HHVy1mvHHzHNy5rAxN/S66Ai8ZZxBfAuRIc9DnHnvSTghBb9CG7MDQxW1rp+RzNBDM1Q/MuY2WrLVuBwrmJieevWZKOgFA0At9KISBUPo2I4ngDrmh4CsQjhC4/CFkifmQCrjJidCRyCoAT1cNNUdIiWavFUslRQAwZrjWaNi8QVxUm41Pjoz6LWXZlBRxdNGVeGsLYG3FLocGBXoN0wrA0UUHKkOQ8CQxCxar3wp2RA6zOwClUJmR4tniCUIq4MIetKBOV4c1F65hkI0F8hGrZlIt4DGi1dGKZdrp2C/goUpVBVvADvCECPMkMJsHcNLoRr5SjCwxH+vvXICGXgc2HE1yHfUdBP6+hPr3jVA8j2m1Ef1dWRFkDQYAWzsUWUW4rvp6NH9LZd7jRYVeBpavGtdWXYtDXcOK55n6mbig6AKUZZXh07ZPcdPGm7D2OPWmja7qCjgCLCtYhmpV9bhWlsdSPBNCcMfmO9Dv6Ue3qxuP730cjy98nLFPsVqCF2+aBTZ7Akop698D3loFHP+UmVwP0AUqzRChlFVIr/2JwmjFs0gJLLoXWDaqSqV0GV0UGkjflzgTRfn2ru04v+h8sE5+TifWadpByIXcGPEcDikQZtN7PapMTod4FvI4mJKrwBt7OqBXCKEQyhCMeLGkUodyPXOS7Aq6IOPRbeWSchwxH0kZLgjQxOZeTy8enPMgXUQc+m1OpQQSwDDxrMijAYMeEyA1AFwRDZ+KffE+WrYs1VMbjnDq8vdKgwzfnV8EtfT0r/ifLjyz7BlsX7U9VsaqFWtjxHO7sx3OoBPXf3J9wjHF6r2r8fDch4cVz6OI52A4iNs234bwUAjuSEQXxwwSA2q1tdjUvun0NHAE4ohnpQidVh+e23YS/Y7UlVoA4PQN4sIp2bj/QuqtOUM/A/rsZhzsTP0M7bB48fHhPoh4HHBG9YlamRANvQ6I+BwohpS7xRoJfry0DLX5TMWzOxCCRDA+L2MAmJQjxzemXSBhGebn1ybcJ1dGJz9AYgK1UF6Iz6/6HFnC06f2H63CypXz8OGhHvxuwzG02lsxyzALFr8FERKB3TcIhShzb8soolUZscA5AD2unjirDWAoTySNBcNkVhsAJQ6+6vkKkzSTMF03HfmyfORIc9BgaRh3G/4HDIetpWFBMPJcbzNtg9lnxvc2fg+EENy34z6saVyDJmsT5mTPgT/sh4Q/PqsNWSiEQCQIeSgEgdsEBFz4vtUCkSZFsPq/GwxTgP764QXd/nqgay/Qsx85ylL0upmVRKkC1cYNTQWttjRMTfAdp9IcIAB8dzfTWkWsoqGFuXXA7buBK18Apgyp4MvPQ77fgy7n2GNL96Abrze+Dovfgh53zzAB1rodKFmCLGHWmF6+ALCgYCl2ciMgkQitmnANUKI5p45W2kl0AP/MzKWZSET9nU83ZuhnxDJGMkWvuxelivhFgOmK6WiwNOCTtk9SknPLi5dDxpNByE1foBP1eA5Hwniz6U3cUHNDxt99JKQ8KUKRUFq5HgBw0nYSHBYHerEeNaqajBXP0aDViUSFsgJN1iZ0ubpilmBRTNFMQa+nF0fMR6ATjY94ZrPYKFYUZ6x6tvgtKe/5kRDzxJDypLFF9ajVBo/Dg5wvjy04nLCeQKWKPkOyFUKG4nksvLOvE5dNz4WQx8GFU7LxiwsqIeZzqQWpNC9W6ZYKO7p3YFHeotQ7TgRIChw7doxcffXVZMaMGWTlypWkvr4+1VsS4sEHHyTbt2+P/b148WIyODhICCFk37595Kc//Wnstaeffpq8++67Sd9z1llnxbbt2rWL3HPPPSk/f9OmTQRA7N+6devIunXrGNvuuOMO0tjYSLRabWxbTU0NaWxsJCtXrmTse/0r15PbHr+Nse2RRx4hjY2NjG1LliwhjY2NZMmSJYztjY2N5JFHHmFse+6558j27dsZ26rOqyJ///LvpKamJrZNq9WSZz7eS6Ysv4mx7/1/v59c9dRVCduk0WhStmn79u3kueeeY2xbdc+qjNr056uLGNvevymb7Pz0Hca2756VQ7o2PUtqC5WMNlneuIX8ejGfse/P//bzUzpPU/80lfzfM//H2FZ4yU/Itq8Pp9WmE/u2kEuu1TG2rXx0Zdx5WrlyJWlsbIw7T42NjSRn6fUpr71fXDqF+P84hRjkXEabLG/cTL5zTm3ceVr24DLGtvzvTCHvfXGAsW3hwoVpX3tnP3g2eerDp+La9Mhnj5DssuzYNoOcS1a+t5KsunoWY9933n2HfPHj4oTnSZqlTnmeWn9ZRZ7/05OMbVfdfn/a157jxcvJfQ/fl/J+SnSeGhsbM+7PxtufFJRVkWuf20pWXHplyntvwR0LyOF65nU6Z+Ec8vpXr4+7Pyk8uzDhdbp+33qivVSbdpvG25888sgjpH3ba4xt58yuTnqd/r/7bo9v07atad97d9xxB2Pfr35WRT586Y8pz1NNTQ0xvXMnuWFpFWPfTZs2JWzTqfT7624uJ43/d2labfrTR3vJtIuY/f67775LbnvutrTalOg8rXx5Jbnr93elbNPUeVPJuW+fS+TT5Cnb9Odn/0xeWv8SY5tuqY7sOrwrrfOU7NpzvHAJ0amGP39yRQnxPTWTfOecaYx997/+EHnrJwsm9Dydif1JJvde0feKyPNfPJ+w/YsWL2Js/83nvyErfrqCue2p35DKpyoTtj+7fPgZkaXOIpevuzztc3oq/cmPH/hx3DmVV8wmT364J61zqr/yl2TL1m2MberFWnLPG5+ndZ3mfvdpUnXrM3Ft+sMHewhPpkrZpvWfbiTZV/3qlK7Th37165TX6fyL5pMfr//xKd17p3LtPfLII+TWj25lbJs9fwF58K2dRFI2m7F9z+E9ZOUd94/r3ou2qeyqMsa+r779Kpn8m8kJ2yRXy+PatPDihXFtuvKRK+PaNPo8lc4uJQeOHoi7n77t/oQQQjZs2PCtnNdvo0/N5Fq99fZbyczXZxKNdrhPERWJyLK3lxH1YvW423Tim21keY2Msd326vXksXtvPe1tOhPO0/HfM+cc2Uuz49okU8tOS5sOvfDjhG0yrf0Js02LF6Xdph/coiP3vX13yvPEzeKSj77+iNSuYs7DPnjt+bTaVF1dTWa/XEMuvuxCxr5fr/0jefvH8/4l99N4+pRT7U8MSw3fynX62dbPxn3vTft/08gLb74Q16YDBw4QiUpy2u+9Dd9sIJU3V8a16dvoTwxlBtLY2EhWXL5iXG2qmls14depSCkis16fRW6+7ea4Nv3u1d+lfe1N9Hl6YOMDGbUpqySL0aY1X62JO0/Vj1aT1995fULbNO/2eeSVna+k1aY5a+Z8axxKSuJ5ovD444+TTz75JPb3woULY/9vamoiN998c+zvxx57jGzYsCHpe0a+9/PPPyePPvpoys9vbGwc9wAuER7f8zhZ+dFKct0n103YMRPht7t/S95qeitu+8/eOUje2NNOCCGkx+Ylsx/7nDx/8HnyzIFnEh5nPG1//tDz5E/7/zTmPs3WZnLxPy4k5A8VdMPvCgl56XxCjv6DkHCYkEfVhAR9hOx/nZBfywnp+oaQP88iZMP9hKwuICQcGj7YP24e3qdzL3nhxTnkyX1PZvy9owiGg2Tqq1NJJBIhJ40usviJrYQQQs5/6gvS2OtI/0CvXULIyS2EfHAHIftfy/h7VP9yA2nqc5D5q7cwX+j8mpDn5xOy5beEbPsdIe/eROxvfo8s++O24X2aNxPy96WE/K6IEJcxtvkXO35BPjr5ESGEkI1tG8nCV24kG+v7yPqW9eTe7fcSQuLP+Ss7W0mnxcPY9umRXnLLa/vIdeuvIwcHDsZ9949OfkTu3X4viUQi5M97fk9e+HMFWfTOIjLw4W3ksrcWkfdOvEfOevssYvFZSOD5heTG3zxPbn/jG/LW3o7YMT4Z+oyEOPwuIeu+F7f5B6/uIxvr+xK/Zwi3bLqZ7HyqjJCPfkLIZ78kkUiEzHtzHtl7eO+Y7xuN8dwb4+1PjvU5ybI/biMfHuwmt635Ju71UDhEbt50M9nasZUsW7uMNFubyfn/OD/jz0mEPncfWbZ2Wexvi89CfIM+Qgghv//692TmmpnkhSMvkF9/9WvydtPbYx7rlPrStTfS/oAQQnb/hZB/3sl8/c1VhDTSa5vYuwn5YyXz9ab/z96Zh7dZnen7lmRZsnZbtuR9d2Inzr4AIZCNQIAGhrDTQmnpOlNKO8xvgLZTKJ0pBVqYgQ7tFGgpUPZQCGELSyAJSYCQ3YkdO3a8b5Isa19sfb8/ji1bsbzG2SD3dXERS98mW/p0znOe93nflKTflUpS0DP+cz9ULkmOurFta6uRpPsLJSkSiT400uvujfRKc56eIwV6AiMeNhKJSIufXyx12A5J0m9yJKl1nyTdXxB7LxyGd/e3Src89ZkkSZJ0xyu7pflPnyO1elqlG9bfIH3rnW9Jj+58VPrfXf876nE+qemU8u5YL730eYN056Y7pddrXo+73faW7dK33/m2JEnitd+79V5p5csrRz1+PK5ed7W0v1MsXId6QlJvpHdsO7ZVSNKBN8S//2+JJDV+LkkfPSBJ790tSVXvSNLTV4i/1YMl4jtHkiTp4wclacMv+/ZZKu61E2S87/UTeT8Zjacrnpb+5f1/kVa+vFK6fv310k1v3ST9etuvpcXPL5bave3R7fZ37peufP3K6M/PHnhW+q/t/yWteX2N9Ottv5YikYj0cePHUvlT5dLaQ2ulG9bfIN389s3R7X+19VfRe0a4Nyx97dWvSRsbNo75Osf72u1+u7Tw2YXR+1c//lCP9Pimw9J/vLYv+lhvb0S64n+3SBsr22O2dfpCUvkv3xly7GtfvVW66rn/HPUaLvvDFum253fGfl/3saGiTcq7Y730by/tHvJclzcold8tzvvWJ7ulZXH2Hy+znlwiTbv3r1Jk0L1qMNtatkX/Xuc9f55k89mO+Zzj5b5P75Oe2v9U9Of+v/mlj2ySlr5wgVTfXS9d/o/LpUOOQ9L/fVwj3ftGxYTOE+oJSfOemSf5w35pXc066dYPbpUe2vGQdO/We+Nu3+pplRY/v1jqGXT/vWrdVdLdn9wds92PPviRtOHIhrjHqLRXSuVPlUfHZJIkSR81fBS9dw7mRNxPJEmSdu7cOaH9Tle+aPtC+vqbX5c+rP9QunbttZIkSdLjex+XFjy7QLrv0/ukVa+skq54/QrpsPOwVP5UuVTrrB35gAGXJP1nxsDPVe+KccevzNLlL18o3f7OdyTpkbmStPNZMXc5BZj075DnrpOk/f8Q/354hiSt/Z4k3W2QQo2fSXOeniOFekPRTX+++efS2kNrJ/f8o1Hxmhgvth+QAr+bMa5dX3m0VPr5hz8dcZtPWz6Vyp8qlzYc2TAwfumsFuPIcfD9P0+X/rD1v8RY/qmvifnkJHIi7inHcj/p6e2RZv9tthTuDU/4GOPhprdukj5p/mRc+wR7gtKcp+fEfA/0c+DAAanCViGd/8L5x+01LH9pufTcweekH7z3g0k53g1vxp/TH02oNyQtfXGpVOeskyRJzGPO/vvZUpe/a8zvkx9/8ONhvxsnSpO7SVr47ELp3OfPHXabTl/nMZ3jgc8ekP66769xnxvutd//2f3D7jMcd2y6IzrHunrd1dK+TjE+/ef3/1n6oP4DqcHVIF3w8gXR7UM9vVLxz96Uwj0jz5F6eyNS8c/elPyh+HPHBz97UHpi7xOjXt9nrZ9JV6+7Ovrz8b6fDJubsXz5clasWBH3v4kwd+5cNm3aBMDu3buZMmWgRLKoqIj6+nqcTiehUIgdO3YwZ86cYfeZNm0an376KQCbNm1i/vz5E7qmYyFNk4Yr5OKB8x84rucJBlU8v6My5jFJkthSY+PcIpGnlaZX4fCGaBomw26iZGozafGO3JjJFXJhUJtECXugG3pCkHeuKJf3O0ClA6VaNE6YsgpSCkTURts+CHkGOvuCiNpIShZNCD3t5KtTxt15OObagi60CVpkMllfZqAo3dSpEmJKXUdFZwV3u3g94wiVBwiEe+nplcg3a+l0B2Mz0jPniBiB5p2idHz6GvYZlzE1fVC5e8ES6KoXOdnagfw0nVIXzSay++3olckiaqOv8Uk8XtzRxI56R8xjzU4/maakYctHcg251Lvr+dW2X7HNtpu/JykI9gZJa6vgx1Nv4Lys80hWiXgPZZIRecjF5kM2LpkxkE9VbNFR0+kZ5hfkBLWJHW07Yn43dm9w9KiNoJPkrAWw9yVIzkcmk1FgLKAlMLZmYieD/FQNjV1+Pq1zRPOdB6OQK3j8wsdZmrOUHqmHJ/c/OWnZ8alJqTiCDsKRMD2RHr6z4Ts8se8JeiO9vFP3DteXXk9dd92QpoKTTusekasMInqn8dPY522DojY0ZtF4cvDnxlELnjbY/ND4z3101MZImIsgEh5oYjcKcplcNIzwjpwX2uZtI0GmIG39v8Kcr0N6Oegz4cjouXSDM55tnhAzU87lhcoXqHRUcth5eExRGwBzc5NRJchZkJ8Scy85GnfIHZNReefCO3l0+aOjHj8eVq01mvN8z7Z72HBkw9h23P132PooRHrBXgvJBSJ2yNkoSqF1FvG3SkqB5h19F94q8p1B/N/VTJu3bcI9Kk5X0jRp7Lftx6qxkmvIZWfHTi7Mu5Bp5mlUOaqi2x1wHKA0pTT6syHRQJu3jUZ3I/tt+3ml+hUaXA0kJSSxrWUb1c5qDtgP4A176fR10uRuisavJMgT+NlZP+O3n/32uGU9v1j1IqsKVg0pZ1UrFZRlGKhsHXg/r9vTwr7mbt46Ku7J5Q9jiNPA7mzLSppD4n30edvnSJJEhztAoyO2bLWt28/3lxTxgyVD75UWvYhr6c93HoxOlYA32IMkSTR2hyhOO7YGyo2uRhSKXgqTi+JmT4LIeG72NNMb6cUVcmFSnfgGmmb1QAbvYIqtiTiDXWTpsqLd6rv9E28uWOeqI0ObgTpBzXzrfFo8LTx94GlumXFL3O3Ttenic2IXUX6N7kaqHFXZd+CmAAAgAElEQVTUHBXb1eBqGJI12U9/nuf01OnRx+ZZ57Gnc89X7p5zsuiPcnm/4X3mmURJ/6UFlxLoCXBh3oVcUngJ01KmkafPQ6fUjd5EM1EHSKLHCohxR+kl8P+qMekyyEyZKuYFDVtFDvGXkf6c55BXfNfO/xYASss0zEnmmGz045bxPOL19UVtOGoJjTNjNldppHGUvgcdfvH62r3tYmyltULDNjFuHQdXo2Ndw3uUJJeIMczgSJCvAF3BLgwqw6Q2nxuJedZ57GjbMa592rxtWDQWFPL4sVfTzNN498p3j9trMKvN7GrfRb4hf1KOl6pOHTHn+f7P7ucH7/2Aa9dfS6GxkHyjOK9cJqc0pXRccRuNnsZJz3jO1GaiUWqG/c4Fjvl+Y9FYop/xseIIOMYVtQFi/NXkbkKSJNp97dHr7m8wWN1VTYmpJLq9UiEnO1nDxqqRc7q7/WGSlArUyvjv2VxDLoedh0e9vlerX+WSgtHjrCaLEYXntLQ0Lr/8cp544gnefvvt6H8TYeXKlSQmJnLddddx3333cdddd/HGG2/w4osvolQqufPOO7nlllu47rrruPLKK7FarXH3Abjjjjt49NFHufbaawmHw1x00UUTe/XHwA2lN/Dy6pcnVeg9mt6IxMeVXg51tlNvH8g6rrV5USrk5JlFLpRSIceYlEh9d1PcDLuJMpaMZ1fIhVGVDJEekc+cnCdy2ToOCoFZly421KfDDS8KYbknAC27YMbVouNwPz475J7TJzx3UKDJ4IjrCEBMV9Cx0h3qRpcgJnVd3jDJfc1qdOoEPMGBzE93IMyBlhEaKfU3GfHZRW7YOOjyhUjWKlErFaiV8qhoBIAiAfLPE/muxmyYdhnPOqaxbKoldptpl4vnB00qdUpdNOO5XzTeWe/EpDYNm/Hc6Q7SYI8VAlqcATKMqmin1aPJ1edywH6ASkclj1/4BPd4elmUNgdZZxXLyr9BmiYNo8qIM+BEpjaQr+3hvCmp0VxLgDyzhqYuP+FBDR1//o99bKzqEKJeUjI/fP+H7LMNNDJxeEOjZq06Ag5SClcIUd6UB0C+MZ/mQPOI+51MVAkKsk1JvLm3NZrvHA+ZTMbstNmsr13PmpI1k3LuBHlCtCnqS1Uv4Qv7+KDhA7Y0byFdm86y3GVR4bnYVDwp5xyC3ykmMOa+41vLRVavr29BJBwQPycXiJ+ValAkDkz+AJwNcPY/w/bHhBg5HO52qHht4OdIRCx2qUaZcA5Glz58Jn0cxpIXemD3Xyhz24VQeuF/igcXfBu2/WHoxs7GmAxsY5ISl18smnW6g1yQcwl/q/gbCzMWEo6EqXJUicnRKKiVCj78t6Xkp2rRJ+rxhOIvDLlD7pgmTEqFMppDNl7SNenRhkTVXdXRzNlRObIFWneLCbA2FbRmkZHoOAzejoFs+NJLoXJ934W3ie8cAEMm+zt2c8mrl7CtdduErv10xZJkwR6wY9VayTPkkSBPYEbaDDKOag510H6QMnNZ9Gd9op4d7TsoMZXw9bKv81nrZzS6G1mas5T36t9jZupMZqbN5JGdj7D6tdW0eFsoNA1Mps/JPIcMbcaEu8uPRLA3yIuVL3LTtJviPl+WYeBgmwtJVPTx0HuH+K9/msHGqk4ikQER0BWILzzPTp2LV2pkd8duvv3ut6nrruNPH9Xyhw8HhMie3ggOb4hii46r5+cMOYbFIL67CuMIzwkKOWqlAm+ol4buMMWW8QvPkiRF80m3tW6jSDeHEsvwTczStenY/DY6/aJz+XCT6+PJ0RnP/VhSXGhkYsJvThINCJ2+8ISbCw7O/s7QZfDy6pfZcOWGEcfGizMXRxtSvV//PhfkXUCNsyYqGvdGemlyN5FriD8JTlYnszJvZYxooEvUkSBPiDaBPsPxxaw20+ZtY0vzFpakLgHE3//pi59mtmU235v5Pe5YeAcKuYI3rnhjdPFAJoO0Umjuy4t19AmGScmYVCYy9FmQNUeMMSxlIx/rdKVfeO6sFGO27AVwxZ8hUSuMSYNynm0+27gFmWMmOR+CHtj7EiH90PvwSOSoU2nwjjy/tflsKGQKGt2NuENuscDUsH3cwvMKVTrvzvs5jy78Dwi5YxrFfxWw+8eXi3uszLbMZq9t77j2afG2kKEduaHfeHKbx0uKOoWdHTsnTXhO06TFNPMdTESK8FrNa6wpWcMvz/4lf7rgTzHPl5nLOGA/QCgSGvU8kiSJ5oKTnPEsk8mYlTZrSL7zZJKWlEanb3xNGCeSZV+aUsqujl1UOirRKXVRc1C/8Hyo65BYlBrEg1fN5M61e4cYHgbT6QmSph9eJzk/+3w+bvp4xKxvm9/Gx00fc0VJ/MbUx4Nhhedf/OIXPPfcc8yePZs//vGP3Hrrrbz++uuEQqO/EeOeSC7n3nvv5YUXXuDFF1+kqKiI1atXc+211wJC6F67di2vvvoqX//614fdB6CgoIBnn32WF198kfvuuw+F4sQPojVKzXHvVvzarmbUcj35FhnvVgyIGQdaXJRnGWLcLelGFS2eFrK0kyeEZ2gzxuZ4VhkgySTEZFOeGKx1VsZO/vuRyUBvBZUeZl4DNYOEZ68dl2U+2z7bTqi7jRxDHi2eFipsFVz9xtXjdo64gi50ij7h2Rciuc/xrFcrYxzPT2+r5763R1jd01n7hGeHcGCOA7tn4LxpehWd7mDsBkXLAQmM2QR7evmkxsay0qPC8ufeBCUXxl5Sog532I3Nb8Put7O8pIjdjV18dNBLV2CoQ7OnN4LdG6SxK/YGtL9rO0mabhQyBRrl0AYXJpWJG0pv4A8r/oBGqWFJYioPpa8Qf9e+5iz9jmfURmakyfn6WXkxx1AlKMg0qqm3D5x762E7f9/eAH4nYbWeQG+A9xvej/m9pYzgeJYkia5AF6Ypl4BMLgagIBzP/lPX8QxQZNHhDfZQnjWy83a2ZTYz02ZOqvs4XZNOm7eNpyqe4vdLf093sJv/2fU/XDP1GgqNhRy0H0SpUA7bRGlMBN3w5IXxn2vbJxy+/cKHIkFM2jr7qjpa94ClFBIG/e01ZlEN0Y+zQSxQJSVHO5rHpfYjePvfB5rjhNyg1AyceyzorcJdPUbStelRV+9wHDy0jrKc8+DqpwauZdYN4nfTumdgQ/theHIlvHyzcHx7OjEOcjx3uoOcm70Ai8bCuZnnUmQs4qDj4JgczwBZpiSg714yRsfzsdD/u+kfqA43KI7B7wR7DRiy4IunIOcs8bi5CGw14OkUC4MgnmvrW7xytQgXORDQWbi9+S3Ozjib9YfXT8prOV1I0whR3qqxUmAsoNxcTlJC0pD3aaWjMtpRG4Tj2R1yM808jenm6VTYK2h0N7IidwUKmYKFGQtZnLWY5yuf57+X/Tfrr1hPgbEg5tylKaXUOsfXuGUsrD20lump04e9L6ZoE9EkKmjq8tPU5SfY08s1C3LQqxOoGLTA3O0PY1APdS+l6XUoewr55dZfopAp2NWxi221duoGLf53eoKkaBNRKmKH0BW2CrY2byVVp0Imi+94hv6qqzCNztCEhOdDXYe45B+XcMXrV/DwFw/zrTmruXv19GG3T5AnYNFYqLBXRN25J5rhHM8qjQ3C4p6VliQmy05/GKNmYs0Fq7uqY5pOymSy6OdgOBakL2BX+y5ANH5dU7IGbYI2ujjT6m0lJSmFpISkuPsnyBN4aOlDyGWx7weTykR3YGgjzzNMPlqllgR5Aj+Z+5NoQ1wQ4yi5TI5KoYp+l43ZKVd6KVS+Kf7tGHCq/mDWD7go/yLImi8Ws9NKRzjIaUy/8NxRKcZpcgXMEnP2DF3s4mWLt2VSm9uPCZlMzCMTVDiLx2fQsGiteHr8bG7aTIUtfhPQTn8nJckl7LPtIy0pTXy+G7aJ8ed40JjF/PHIZshdNL4x6JeA4YxNx4vp5ukcsB8Ys2bQFeii1dM6qca98ZKsTqbd1x51Hh8r/Yu48Wj2NKNVarkw/0JmW2ajVMQu8pallPHEvif49s5v4wyM3Gi51duKVqk9LnrYhXkXcnbG+BZ5xkOaJi2mamMs2AN2UsZZ+b44azEH7Ad4sepFlucuj2p36Zp02n3twvF8lPA8Pz+FC6dbee/A8A3rbe6Rhed0bTqzLbN5r/69Ybd5tfpVLsq/CKNqjJXAk8CwwjMI4ff888/ngQce4P777+eTTz5h0aJFJ+ravnJ0+8M89clAtMTGqg7OL87DpOvhnf0Dk8SqNjdT02Ndexa9EkfQRrr2KKH3GLBqrdj9dsKR8LDbdAe7Rcla/mLY8rBwPKdOEa5F26GhwjMIF2H6TMiaB+0VA2X0PjvbwyVYAnUcrDlMoj6DdG06L1S9gCfsoTs4vgG8M+iMOp6dvjDJ2vhRG2/ubR0qCB99vZ6JRW10+QYE1OGFZxkYsthe66DEqiP1aKdv5mxYdV/sJSl1VNorWfHyCt458g45BisPXDWT13Y44/6e7N4QksSQ1bOq4Cvsdb8+7Gq0TCbjrrPuGhioa9PE4GnQQNuoMopzqgysmWbg3OKhg/qiNB2H++I2AuFemp1+Pq2zE3DbcCvFZO6D+g949sCzPFPxd4I9vXFFgX58PT4S5Amo9Rlw42vRCUGB4dSO2gAoseiYlmkYtjymnxvKbuC/l/73pJ7bqrVSYa/AE/YwLWUay3OX0+Zti37xGFSGY3c7OxtEfEa8CXfrbsiYHfuYIVMIhSD26xcX+9GYwTtIsOhuBFOu+Js7RhC2HLXic9veJ0aOJ2ajH126cE6PEavWSpNn5OqMql6vEJ4Hl8Ur1bD0Lnj+eqj9WDy27lY49zbx77Xfgd9PxYCXbn8YSZKweUNY9Ek8svwR1pSsiQpxFs34ujyXppTyeXt8Z6o77B69JHmMZGgzaPO14Qw6o4tmo9KwXXxP5J4De14YiGjRpIjFifb9ois8iBinriPi392NYBSLsLVKJToJfn3ur/mo8aMxd/r+MtAff2HVWFmRu4KHlz0MEON4DvWGqHHWxDjZ+//m01Onk2/MxxFwcNBxkEJjIUtzlrIkewmri1bzuyW/G3ZiUGAsoM418aisePjCPh7f9zi3zrl1xO3KMgwcbHWxv7mbGX0LfCtKLbx3YGAc5fL3xHU8J2sSkXxTqeuu48ZpN7K9ZQc1He6YqrPW7gDpxqEi5HOVz/HSoZdQKuR877xCCtPiC896tRiDNE7Q8dzkaWJx1mL+4+z/YN0/rePiwpUxVUbxyNZls69z37EtKh4D5iQzDr9jyONheStudwqSJA1EbfgmHrUx2PE8VrL12VGTRb2rnhJTCcXJxVR3VUcfy9PnjXSIuJhUw1egnWFykclkPHPJM1xefPnkHbT0a0J4lqQY4bk0pVQs4GQvAIUqWnH3pcOUJ2I2tv2vEKEHkanNjFYtuUNueiI9JyXCh0t/B2v+TGiEkvx4yHUWspV6bv/4du7eendckbLT30l5arlY0NdaRbWezz7+hQZNihCe6zZDwXnj2/dLgD1gP6ExLOYkMzqljgZ3AwDesJdwrxg3t3haYv7W+237uWjtRRzqOnRShef+BeE8w+TcS1KTUuNWGMFQo8HRLM9dzm8W/4bZxtlsqB85Eq/KUTXhKsjRuKTwkuPqxLVoLCPGkcTD4XeMexFFnaBmRe4K1lavZXnu8pjzH3YeZp9tX0zURj/FFj1HBo07j0Y4nkd24a8pWcOr1a8O+/x+234WZZ5YXXdE4TkSibB582buvPNOvvnNb5KTk8Mrr7xyoq7tK8euhi5+/ebBaBbw9loH83OyUCT4qW730O0TAnBlm5vS9NjVJYPeR5LCMGTl6lhQypVDcryOJup4vvhBEaFhyhOCQMYsMWCLJzzrrWIQozII8SXQLQY3wKvtFlI1cqxtH3HXhnaSZOm8VfsWMmQ0e8cXodDiacGYYObi/9lMpycYjdowqBPwBHtwBcLUdHiot3uxeUYSni3Cve13CpflOHB4BwvPajqPPk9KAXz/Y1Cq2VpjY+nUsYlGukQd21q3cVXJVawpWcP01OlMseppcvQgIeHv8fN51+e8Xy9cxB2uIAZ1Ao0OH46Ag9dqXqPR4SOi6OKTtvfGfiPVWUXpe+rAxC5Z3ZcrrdJzf+tG9tv2D9mtyKKjpkMIz3U2L7kpGi4os9Le3o5bqSJbl004EuaxPY/xyqFXSNEmDptXCX0xG/3OrcIlURGvzFw2xHV0qrFkShrXLhi9fEilUI3q1BovVo2Vjxo/YlrKNGQyGdeXXs9dC++KOrkKjYXH7rDuF5GdDUOfa90j7g2DMWSKTF6ILzxrU8VgH8QE0NkwSHgeIb/KUSsWSqr7VnsDrvELz+N0PK/MXclLVS8Nv0gWidBMDzlp5UOfm/dNWP0IrL1FOHlb98K8b8EF90DLTtBZ0AZaCfdGsHlCqBJEyf7UlKlolBqKTEUkJSSNXSiWJGjZxVkpM7D77VGRZTDukBtd4rFl0PbT77JtcDcgQzY24fnIZrGomTUXevyx7w1zMTTtAF3fZ8SYIxzwXjuE/aAXZZMNCsgN92De/n/MSi1nU9OmSXk9pwPqBDX6RD1WrRWlXBmd/A0WntcdXsdcy1y0ygGRtN+9Mt08HblMTllKGTa/jWx9Nr9f+numpkwlRZ3ChfnDVDYghOfJdjz/Zf9fWGBdEJNHHY+yDAP7W1zsa+6OVpZcOS+bF3c0RiOfXIEwBvXQ8ZJJo8TXNY1vlX+Ly4ouY0fbTs4uNNPtD+MLiQXrtu4A6YaBBeL+WI9tLdui2cD/b1UJ++27o5FYg9GrReVCsytM0QQynls9rWTrsplrnTvm74glOUt45sAzJ83xnJoUP3Oyzd+AvCeddleQVE0qrd5WnP7QsUVtpIxPeM7QZtDubcff48cdcpOmSaPEVEK1c5DwPAFB4IzwfGIpTSmd3PFf2lRQJkHT530L3ke9B/LPhfP/TVRufRmRyeCCX8Lin8JZ3495KkOXEY1ibPEIt/NIY/ZTDo2ZH2mn8OLXXkQmk/HOkXf4ov2LmAgwm99GubmcnkiPqCRr/kKMReTjfI9pzGJR/MhmEa/4FcPmP/H53+Wp5dG56B2b7uCa9dfwk40/Yc26NVz86sU4AmIRdEP9BsK9YdZWrz3xjv1BJKuTSUpIGnPF4mika9LZb9tPuHeocfCg/SCl5uHHUFqllmW5y1iWuoz1tSNXCVZ2VVKafHpWfPRHbdj9dh7f+/io2/dGeukOdk9o8X510WosSRZmps6MPpaly6Kqq4pLCi4Z4ngGKEjVUGcbQXh2B0kbJZJ0cdZiKuwVBHvja1xt3rYT/r4f9u75q1/9issuu4yNGzdyzTXX8Prrr3P77bfHNAU8w+RS2+mlNyKxfm8Lhzs9qBLkzEjPpc3bSlZyEk1O4dSqbHPFNqADNGofatnkO1mOzvE6GlfQhTHRKCb/N78Fs68XT+QsEE039HEyk+bcCOVXikFNv9vRa0PSpPBJbTcJK35BOnZKioqQhS1EpAgLMxaOeB3xOOI6gk5m5WCri/cOtMc0F/QEerjqj1tZ/egWrpibhdMXpmdQBnEMOqtwbyfqxj3A7BosPOviOJ4hKsRVtrmZljE20Uin1CFDxs3Tb+bfF/w76dp09GolmsQE9EqRuVzhqohmbHa4A8zKMWHzhNjRuovH9z7OpupWZAo/4Uh47IMCXZoofT/K8ewMOpFUet72HhnSlAdg6dQ0nvu0AW+wh+oODyUWHTeclUu3ox2nXIFBZeCec+7huUueo9XbTmaK+LL8oOGDuFmwXYEuklVD3++Zukz+reTfxvZaThJnFZqHxJGcKKwaKzvadjDNPA2AIlMRq4tWR59fmL6Quda5x3YSV9/fK57w3PyFcPAPRp8BrlYhhMZ1PKcORG30l30lmfqE5xEclY5amHcz1PRFuJwAx/OMtBmsyF3Bbz/7Lb3x8qd9dtoTErAOl1tWcoGY3L73SzGpVapFVcStO8FShszdhiFJyeFOz5ASqyJTERaNZeyTv7+sgsdXoKh+l9VFq1l3eN2QTSYzaiPfmE9tdy21zlqmJE8Zm/DcdUQscmXPB5Ux1mlkLhFidL/jWakWCw21GyG1JLoY1SgFyXHbYcvDlChNNLobJ+X1nC6ka9OHDCwztBm0edvoifTw5L4n+e7M78Y8b1KbKDQWRuMzppunY0myDBs1EI8CYwG13bWT1lytwl7By4de5t8WjH5/v6DMwpt7RVPB8kzxmS9NN5Bv1karx1z+cFyXcJJSQW/IxD/PvI0iUxHdoW4SzO+Sln6AIzYxBmvtDpDR53iu7a5lxcsrWHd4HUq5kjZvG76wjzXr1nDbxtv4W8Xf2NO5hxvfujH6u9CrEzjQ4sKglqNVjV+0avG2jNuddeO0G3l0+aNcWXLluM83GaQlpeEJeYYI8bXdteTpCzjc6eHsjLPZ3rqdLr8bU9L4ozbcITfukHvcEyl1ghpdok404tRakcvkFCcXc6jrECCE5+HynUeif2x0htMUmQxmXQ/v/lzMA5RHucvURljy7yfn2k4UC74DM6+GhNjxRpY2K1ol0Oo9uTEFE0KbxgpJRYGxgB/M/AE/2/IzHvz8Qa5dfy1v1b4FQKevM9ow1KqxQtv+Ic7vMVF+peg/4bOLviZfMU501AaIZoAV9gq6g93saN/BtVOvpTi5mI+v/ZgcfQ77bfuRJIn369/nx3N/jL/HT4Zu5Izn44lZbSbPkDdpizeLMheRocvgvz79ryHPVToqR128B5hlnEW9q54G19C5XLg3TLg3zCHHoePmeD7eaJQaEuQJvHPkHR7f93j8OdsgnEFntHfDeFmQvoA3rngjpr9GvjGfT677hJ/M+0ncBdN8s3Zkx/MoURsgDGy5hlxquobqMiDu3ZOZlDAWhhWen3/+eWw2Gxs2bODHP/4xixcvjv53huNDnc3LsqlpvLarmW2H7ZxTZCZTm4m/10+qMUSrM4An2IPNEyTfrGVXxy4ikhBL1UleZJHJz9jJ0GWMLDz3O55BZLP2O4JzzgIpIgZrRzPlIrAK4SsqPPvseBNMlFh1aOddB7NuIKtoJj2BNGZZZlFiKhm/8Nx9hCRJiBJ1Nm80a1mnTsDuDXLE7uOl75/D7SunYkxS4vANk1+us4C3c9yNBUE4nkfMeB7EoXb3kAWF4SgwFvCNad8gxxArYOWZtSQp9HQFu3D3uKOTng53kAyjGotBRbW9iWZPCx8drsKgNLMwfeHYGz/0izyDungnq5JxBpy0yWXYI8HoSvJgFhWlsrAghYfeO0RNn/A8Py8Zk8zHZ+1e9Eo9i7IWkaXLpddXxKIZ4hhPVzzNhiNDS326Al2Y1CehrO80x6q10iP1RIXno/nh7B+yKn/VsZ1kOMezq1U0k0w7qsTLkAnuFuiqA7lSNNIcjCYFvLaBY5r6BIDRoja66mDON0T2fE+wT3geZ2yEPn1cjmeAn8z9CW3eNn7y0U+GDGR8XXUEZbKR87RmXgN7noOSlQOPyWQis9jVjEGdwO5GJ3kpsZnsC9IX8JvFvxnbRQbdwn0+72bw2bm44OJodcR/bv9PNjZsBPqEZ+XkfK+kqFPIN+TzWs1rzLbMHrYMMAZ/l3ALpc8UlSGDnUbmPme+blCVSHK+cLgPqshoCHvIXfSvMOUizBEpbs7sl5k/rvgj5amxk12r1kqHr4ONjRtJTUplnnVezPMqhYrX/+n16AC7PK183MJb/0Qz3vfBeHnj8Bv86IMfcceCO8YUJTM3N5mIBNsO25mRPfBZu3lRPs9sqweE8GxIGjqBkMlkGDVKXP4wcpkcdWgGIUUTHt1aKjtE9Vdbt590oxp/j5+fbvwp5anl3LP1HhZnLSbXkMv62vXIkPHXi/7K2kNr+dOeP7HPto9dHSJH2KBW8mFlB2VpE2tW1OppndAkeVHWIs7NOndC5zxWFHIFeYY86roHFgvDvWGa3c1MNRdQa/OSmpTKfOt8XPIdGCfgeG73tpOuTZ/Q5D1Dm8EX7V9EBbSF6QvZ1rKNYG+QI64jE2r6ZFKZYqpfJElis23zuI9zhpPIWd/vi9koGH3brxCDHc/NnubTUHhOFePKsJ8VWYv5/Ouf88LXXuB3S37HH/f8kd5ILza/jUxdJma1WURttO8D6wSE59QS+M77ojHjeN3SXwJs/hPfeLI8tZw9nXvY2LiRs9LP4rrS67h1zq2oFCqmJE+huquaqq4qIlKEb07/JvOt8yk0Fo5+4OPENPM0vlb4tUk7nkKu4P7z7mdL8xYO2A/EPDda1EY/CfIErpl6DY/teWzIc3duvpP/2PofVDoqT1vhGUTO83v17+Hv8VPbPXKF3rE69+P10Yr3WD85KRrau4MEe+IL4p3uIKm60Rfoy1LKOOgY2sfMF/bh7/Gf8Cq4Ye+AlZWVbN++nS1btgz57wyTR3iQy7bO5uXGc/KISPDLdRWcW2xGJpNRmlKKWttOqytAVZu7LxMwwi3v3sLmJjGIVST46A3HzxM8FjK1mSM2GHSFXPFLu7P7sjjjOZ4HY8gSDkmfHZfcKIRXuRyu+CPpGVkEnLO4/7z7ydJl0eQeOTv1aI64jqDotURdxP1RG3q1kj2N3eSmaJiRbSRZmziyKJyUDIrEiQnPo2U899HtD9PtD0cbfo1GniGPf18w1GmRZ9aQgB5n0ImrxxWd9HS4xMpYboqGNyoO0Cv1sLnxC7L1GXxj2jdYkr1kbC+oX+QZJOz0l5Pu6xHnipfjCPCLS8v4x65m3j/QTrFVj0wmw6L0sb7GFi3nf2NPC2mKGTgiokSqydPEPtu+IcfqCnadtJLh05n+lc3hhOdJwdUs3Khd9bGP138CeecOHXj3O56bvhDO1qNFg8FRG4OFZ3ORaMAXD78Twn3RP6Y84dIPTiBqQ2eN73he/6/C/RJvl0Qdf175Zzp9nWxs3BjzXIejGqtMObIwMn0NJKiheMkVE5sAACAASURBVGXs44ZMcLViTFKy9bCd6Zmxr0UpVzIzbSZjoqteiLTaNPDZKTIWYQ/YcYfcbGnewgtVLwCT63gGWJqzlJ0dO5lunk6gN0CgJzDyDj67uO/KZEMn/qklgCw2dz+5AGre63tO0OhpJDf7HDBkYe4Jj03w/hJh1VqHvN8SFYmYVCZeqHyBSwsvHfUYF+RewP3n3z+u88pkMgqNhaMO5kfDF/Zx77Z7eXT5o1xSeMmYz33l3CyStYlYDQPi7ooyK4c7RbyWK9ATN2oDwJSkxNmXpe5uuoo/LP8D2ao5rKt7HujLeDao+bztc0wqE/+z7H9YkbeCi/IvosRUwjMHnmFR5iJKkkvI1mdzwH6AH8z6Aa/VvAaIqqtth+3MSh+7g3wwLd7JbSR9oig0FsYIzw3uBjJ1mRSnmajrFM6ey4uuoFe3HV2igipHVUwTzNH6fHT4O7AkjS/jvp9MXaYQnvvc0pm6TEpTSnl87+NUOiqZbZk9yhGGcnTUhifs4fEjo5f0nuEUQqWHC+6GvDOmq8H0V81EpIhYCNOePLfohND0Cc8f/Raev54EmXAinpV+FsnqZF4+9DLhSBi9UkRVDTieJ+hY1qfDlOGjqb7MOPyOEx61MTN1JsGeIL/97LdDIsFKkks41HWITU2bWJazDLlMzl9X/XXc/VEmk6kpU/nm9G9O6jE1Sg03TruRv1X8LfrYn/f+GaPKOObP683Tb+bT1k+psA804NzZvpO9tr182vop9oB9Qv0PThUsSRZ2tu8kz5AXNyZ0ME3uJrJ12SNuM5koFXIyTeohvbn6ERnPIzueoU94tg8Vntt8bRNeqD8WvnpLb6cQ7kCYc+77kNZuPyCE5xKLnjduXcwXv1jJZbPExKI0pRRJ2URbt5+qNjel6QZavC2EI2GeOfAMADKFB39gYpOYkRi8qh2PaHPBo9GlQclF0WYcwzLI8eyWG6PuYIDcFA1N9h4sGgtZuqwRBfCj+csnldh8dnqCySwqMjM7x0RWsvj96FQJVLa5KBnU1CdNr8LmGcbxLJMJAWqcjQUBurzhGOG5YxjhubrdTYlVj1x+bDeAfLOWSI8GZ0A08Oqf9HR6Alj0anKSNdT3OVLPm9lNrjGTRZmLOC97jLlj2jThvBzkHDWpTbT72tkX6CCXhKGOwrrN4LVh1qn4l2XFHGgd+N2retx4kHB5hfPsYKuLpXlns7N9J8HeIJ2+zrjC85HuIyf0C+DLQqY2kxR1CjnDRT1MBq4WyD17qOP5yGYhPB+NIUM4ntv2QkYc4VQzjPCcnA/OeohXHtVVJ4RKmUy48zsOTixqI57jubdHNLo7MrxzTalQ8t0Z3+XJfU/GRA20d9diVQy/wg2Ie+ftVaJR62AMGcLxnKRkxxEH0zPHkeX86HyRcd1P1xHx+9OYwWdHIVdQklzC522f0xXoosJeQaunddKF5/4FrjxDnmh+Mpr72GcX1xiP1CnifjQo/shjzORxZZiIeaBBZqOrUbzfDZmYg76vnON5ODK0GXze9jnLcpaNum2CPGFCk7ICYwFVjqqJXF6URncj2frsIa7t0bh+YS53rootJ01MkLN6ViZrdzYPG7UBIufZ6QvT6Q6SqJCTrE3k4qxvstf1Fke6j9DlC2HWJbLftp+51rnIZDJ+t+R3LMxYSElyCUdcR6LO4p/M+wk/O+tnXDXlKt5veJ/uYDd6dQKh3gizMiYoPHtaTmpZ8ETpj1/pp7a7lgJjAQWpOupsHrYetvHTpwIoE3p47fA/+OH7P+Slqpei23/jrW+wtWXrsMfv9HVOuC9CujadPZ17Ypyba0rW8H97/4/vzPjOhLq+Hx21YfPbSFaenOaOZzgG5t4ES+842VdxStEfT2Pz28RCmO40WwjTpopK1sZPRYZ3xT8AsWh5x4I7+O+d/01qUioymYwfzvohC8zTxdjWPDSL9QwjczIczxqlhmcveZbvz/z+kDFOSbLI79/eup1zMs85odd1ormy5Eo+afmEu7fezQ1v3sD62vX8eeWfxyw2apVabp5+M88dfC762GN7HuPWObfy/ZnfpzSlNCY+4nQjTZOGUq5kTcmaGHE9HvXuiUVuHQv5qVrqbMMIz2OI2gDR+6rSUcmW5i0xsSltnrYTHrMBZ4Tnk8rb+9qweYJsOtRJINyLzRMks8/xmqJNRNEnQpamlOKlgVZngKo2F6Xpeupd9cyzzqPOVUeVowpJ4cHjVeMPjZxRM14G53jFY1jHM8DXXxpo/DQcBlE+jtdGF/qoSAtEm8t0+8Nk6kbOmj6aj+oOkiS34AxIWAwqXvuXc8kzC0e4QZ1ARCJGeE4dLn+5H51lQo5nuzcYfU2WERzPVe1upkygu/3R5Jk1hEJJdAW7cPW4BqI2XEEsehVnF6WQkxYiR59DvXfv+G86WXNh+S9iHppunk6oN8Qr7dtZ0pMwtLT63bvgM+HyuemcPL57XgGFaVoI+5FJEc4tS2Z/Y4jeiESDw0e5pZCuYBc1zhqydFn4e/x0+mKbEu3u3M0sy1FN6s4wKlatlbfWvHV8VzhdLZC3SIjCgznyiWgUdzT6DOEqbtsrIhWORmMeiNpw1A00+EnUimoEV5z7wuDSWEsZdFYJ4Vk1zqiNeI7ntr0Q9oqoihFYlrsMT9gTs3DS7m7COhYBIylOjIwhC1wtGJOU+EK9TBur8OxpB3u1aJDaT1R4TomK+qXJpbxW8xqlKaWsyl/FusPrYpoLKgKO4R3mY2RK8hSWZC+hyFREalLqyDnPkiSiNoZb8EudAt96O+ahl3tsPJps5EXfEQD8PX66Q92iTNaQhdnf/ZVzPA9HujadGakzxO/mOHFZ0WU8vu9x3jj8xoSP0eBuIFc//sG+WafiynlDFyevmpfN85818GmdY1jh2ZiUiNMXorrDQ1Hf9/LszEJSQl/jF5/8gm5/CINayX7bfsrNsYJ4iamERHliNL5kjmUOF+VfRGpSKpcWXMpDXzyEXq0kw6gmUz/+rMD+8sgTnZk5GRSYYhtOHnYeFlniqVrqbF7W723lh0um8OdLf8M92+5Bo9REc5Z9YR/1rvqoazwenf7OCbvWMrWZ+Hv8MQLa8tzl3Dz9Zq6bet2Ejnm049nmt2FKPBMRdoYvB/09gE7LhbB+4bl1L1z2CGx9JPrU9NTp3LXwruhi59KcpSQ7WyBtype3keRxxBY48c0FQSyOfKv8W0PiDIqMRTS4GqiwVTDfOv+EX9eJRJeo408X/Iny1HJ+NOdHrL1s7bgXZy8uuJiNjRsJ9YbwhX3s7dzLBbkXcO3Ua/nTBX86Tld+YrBoLExPnc4cy5y4JrfBNLgmNhY9FvLNWo4M02BwrMJzaUoplY5Kbv3wVv6w6w/Rx1u9J6dSZVThef/+ka3nZ5g4a3c2cUGZlU2HbNTZvOSmaKJi82BKU0rpDNXR2h1ge8cG9vufpd5VT7GpmPOyzmN3x26cQQdWbSoH21xxzjRxRs14Drom5ASJ0iem4LPjkAzRBoAgVp5zUjQ0OHxk6jJp9jSPuVFRm68RRa8Fh68Hiz42R1GnFgOHEuuAk2+0/GV01uGddyPg9A24qtL0Kjo98c9xqG3s+c4jkW/W4vWrcAaduHvcA1Eb7iAWg4or5mQjV3azIH2BCJXXjFN4TkqGOV+PeUidoOahpQ+RojKyJNgTKzyHfMJtuv8VkCSUCjk/v3QaqgSFiENISsZiAimiprbTQ4PDT75ZT74hn02Nm6JOt8FfCOFImAP2AzHdYc8wdrTKyY/kiWGw47n/82o/LERE6/Sh2yuTIFEDDZ/Gb9wyOGqj46AQkvsxF4MtjqOyZbeIXQDheO6shOadMRExY0KlBySRidxPw3bImjeq8CyXyZmfPj9mFb3d2441aWKOPJGF3YohSYlelUBO8ijO6X7sfU0lvB0Djx3leAYoNZeyqWkTZeYyVuatZFPTJtxhd3RhMbnmVdj0u4ldex8ymYw/rPgDRpURc5J5ZOE50A1KDSQMk2Emk0HqgLM51BviWfsOHuh08NjhtbR722lyN5GlyxKNOwyZmD32M47nPsrMZXytaPIyBeMxP30+9y2+LxrdMhEaXA2T6jKZnmngR8uK+fdVU1lcEn8ybNKIqI3qdnd0gdqiV9PbfQ5t3jac4Tb06gQq7BXR5lP9zLHO4Y6Fd8RtxHjb3NvY2rKVoKKO80pSJ7QA2D9ZONHlkZNBobGQOtdA1EZtdy1FpiJyUzS0dAfYdKiTpVPTOCtjPg+e/yAPnP8AVV3i/l7XXUemLpMtTVtwh9xxj9/h65iw8Nw/ARs8EVMpVNw+/3aUivHnTcNQ4dnut2NUHsN4+QxnOIXI1GXS6m2l1dt6+jmeVQaI9IAxC4ovgI7KmOq5y4sv53dLBo132vdNrLHgV5xwbxhv2ItJdeosuKkT1GRoMygzl42YsftloTy1nKunXM2izEUo5eP/LrNoLExJnsInzZ/wRfsXTDdPR6PUIJPJTvvf38y0mazKX0VpSim1zlpCvcNUvtNngjjBjufCNC21RwnPhzs9vL67mW5/GLN2dOFZq9SyMn8ljyx7hC0tW6JGvlNWeH7yySe55pprePbZZ3G5JlfU/CrT2u3nULubu1dP45PDNqo7PMIFGocCYwHOUAct3U5afc0ccn/GkW7R7CRHn0OTpwlHwEFRSjoVzSNn4I2XDG0G7d72aBPDwUiSFNtccCL0R204DtMSMZKijb0p5vYJz/pEPUq5kq5g15gO2xVuJuQ34/D3DlkR0vflOpZYB0Vt6FTYhhGFgQlHbbgDPVHhOUWTiC/UgzfYM2S7g21uplgnR3h2upW0elqJSBECPQHCEVEybNGriUgROnwdUTfWZJVZFBgLWH/h38gLeGMzntv2isFapGeoUOfvArUJd8hNus5EZZubRoePnBQNJcklfNT0Edn6bGamzowKz72RXqocVeToc6JOzDOcQgRc4m+dXCBE50DfpHvb/8L8b8FwJVmGLCE+x8uE15jBZxPH6zgQK16nz4jNWo70wubfi7LJeTeLx9JKRYPBI1tEY9PxIJOJuI3BbuGGbTDvW8J9HYpfAtVPsamYw84Bl3B7sAvrRJvw9DUXNCYpOdvSi3zzg2Pbz1Yt/u8Z5NzuqhskPIvPa2lyKb1SL2UpZcyxzOFw92G8YW90oULtrI4VrwcTicBfLgbv2EXd1KTUkd3HfsdAs9ox8Hbd2xQnT2HVjJv5WuFqnqt8juqu6oFYGUMmJlc73pCXcCQMe1+G35cOjYT5ivCdGd/h+tLrj/t5MnWZOAPO0TcchgZ3w6RGA8lkMr65KJ/LZ2ehVsa/H5mSlHT7wtR0evp6aggxutvfS2lKKe7eBvwRG3KZXGR/DsKQaOCaqdfEPa4uUceynGVY09q49/IBp/Tvd/yez9s+H9P1t3haTr9GXn3kG/JpdjeLzx9Q66yl0FhIYoKcDKOaQLiXqX3joFUFYjLoDokF9GpnNbMtszkr46xoI9Sj6fR1kjbBhb1+x+ZkCmhGtTEml9rmt2FSnjoCzBnOcCxk6jLZ2b4TX9h3+vVckclEjFvWfGEw0FlGblZd9Tbkn3/iru9Lgj1gJ1mVLBb/TyFKkks4J+PLHbMxmazKX8VrNa+xrXUbZ2ecfbIvZ9JYkbuCG8puICkhiVxD7ojRcPWuevIMJzbPusSi51B77EL73a9X8Ks3DmDSJMY1q8bjt+f9lvOyz+Pi/It5vrKvV8mpKjw//PDDPP7448hkMm677TZuv/12Pv300xNxbV9qajo8lGUYyEnRYNWr+dmr+1hRFr/kVSlXkqJOodHdgUzhp9FTz472HeQZ8sjWZ9PobsQRcDAtPYv9zZO7ONCf43XEdYR2b2zJub/Hj1wmR6UYfcVlWAxZwn1X+xEfSgtiMp4Bcs1CeIa+1fUR8qb7iUQi+JW7cDqycfh7sRwlPOtUCchlUJA6IPSn6hPpdAdxB8LxD3r2P8Os8ZdbuvxhDH3Cs1wuoyBVR21n7OqVP9TL/uZu5uQe+4TEqFGiQEdNVx0GpQF9op4/b96HwxsiTa/CEXCgT9RTZCoCJk94BpCpDaT4XDiCjgFnevMXwh1afhVUvCrEw9f/RQiUAeF49oQ9ZJtS2FZrRyGXYUxSUmwq5oD9AFm6LKamTOVQ1yEiUoSVr6zk9zt+z+y08Tf6OWl4OuHx5Sf7Kk4M7laxmCSTieZ/7QeEGLn/FVjw3eH302eAtXxoY0EQjmdPpzi2TCZyfftJnwFtfW74oAeeXAk1H8K33xHnB+GK7ndhx4uwGA1TLtT35YpKEeF4LlwiGti1j5wJVmQqihWewx6sxvzxXwOIWIxwgFxthOVmG3z257HtZ68BmVz8DvvpOiKiSAY5nouTi5HL5JSZy0hUJDLXMhe1Qk2CXFSIqJyHYsXrwbTugoat4v9jZNSoDZ9jXFUmL1W9xPXTboRVv+GGsht4tfpVHt75MNdOvVZsoM9A4WnDqDLiaNkF79whHNVHN8E8w6SSrE4e84JxPCbb8TwWTBolXb4Q1e0eSixCCDUmKen2hykxlRCQN1PvraLcXD5u53GeIY9GT0OM6P123dvs6hjbZ6e2u/b4ZvQfRxIVieQacnnu4HNEpAj1rnoKjKIypSBVyzlFqTF9LuQyOSUm0QjqsPMwxaZi5lrnDpvFeCyO5yxdFonyxAlnRMcjWZU8NGrjjPB8hi8JGdoMXqh6ge/N/N4pJyyOCW2qiA8EMf5sH6bC290uKvLKjm+F0JeR/bb90fnmqcQdC+7gG9O+cbIv47RhddFqGj2NvFz1MmdnfnmE58GUp5az3x7/HuDv8dMd7D7hmcil6XoOtbmjmsrWGhuNXT4+/n9LeeKb44+JuWXGLfyj5h9sbd5Km/cUzni22Wy0tLTQ1dVFcnIy77zzDnfdddfxvrZTnjqblx8/P/aJ9mD6u6IDPHDVTN64dTHXzB9+MpGSlIxWHUSvCaOQKahx1pBvyCdbn02TWzie52Zls79lch3PIHK8vrfhe9y7/d6Yx0fMdx4rScmABNOvoDGgGiI856Rooh090zXpMR3Oh+Pt2g8B0EszaHGFhzieU3WJPPuds0TcQx9pOjX1di9LH/yI7bVxXHhpU8A0vsleb0TCG+pBrxrIBCtK01LTGbt69WmdnfJMY9SJfaykapKpc9WiREu3N5GtRxr5+3fPQq1UiFJ/jTU6cZ3Um06ilsTeEEkKNa5Q3wJIv/A8ZRVUvy9Ewl3PwoHX+/JbTbhCLgpTUnn/QDu5KaJsp3+gkq3LZoppCtVd1TS5m+iVemnxtLAgfcHkXffxRq445mzc0wZXsxCeQQzSK16FHU9C2WWgHyFL1pAxfBmj2iialO74C1imxYrTg4Xnj+8XTuub14NxULZrgkrsP+2yib2mC/8TPvgVtFeg6dgpnDHGHMiYBa27R9y1yHiU8CwFsSYXj7DHCMhkYMjg+rJErpumFvmEgTEsNNprxO+p360c6QVnoxDU+zOeJYmkhCQeWvoQxSZxfedmnTvQWNDvROlrB88wjueqd0CuHPhbjIFUdSodvmGOByM3FjyKSkclHf4OzssSTVJz9Dmck3kOlxRcMtA4NUEFaiNmlRF741bIPy/293KG44I+UY8/7I+6XMfLRDOejwWjJhGHV2Q89zuelQo56gQ51qQCEtTtfNH+GXOtc8d97HxDPkdcR6I/t3paafe1U9ddN/xOg3iv/j2W5iwd93lPFR5Z9gjrDq/j4ZqHSVYnR0t1V5RZuWLOUCf3lOQpVDmqqHZWU2wqZkqyGA/Eo8M/ceHZqDLyxhVvRBfaJgOTynTG8XyGLy0z02Zycf7FfLv82yf7UibGnBsHquCs04RRIh77X4HSS0VfkTOMid0du2n3trPhyAZW5q082ZczBKvWevxjB79EaJVaHlvxGEtzljLdHCcy8UvAdPN09tviC88NrgayddknfIEtWZtIUqKCZqcfSZJ4cEMVP71gCnq1ktk54x9LZOoyefD8B7n949v5vO3zE27qABh1hHX11VejVqu5+uqrue2220hMFMLgLbfcctwv7lTnULubz+oco28Yh/buAFajEJ5njeHNY1KZMBt6UKqCTE2dx86OnWToMjCEDTS4Gwj3hpmfk83hzkMEe3pjRNVjJceQg0apYXfHbiJSJPrBc4WOMd8ZhJiSezac9QOcOxpI1sYKz9nJSWyoEGKzVWulzTe68Pz0gb9hDFxMplmL0zu0c71MJmNRUWy2Y6o+kT1N3agS5Gyu7uTswmNv3OMJ9KBVJcQ4eIrSdBzuiHU8b662cf6UyWu8kKlLZU+Ph0QpmzRNEv+6PJu5VlGy3uZtw6q1Ykg08JvFvyFZNYkd1mUyUOlJUZlwBBzivdH8BSy5A1KKhCi5++/i33tegNk3QFIy7pCbksw0OtweFuSLcr0Sk+gcnaXLIkufhTPoZEf7DmamzeShpQ+RIDuNGnyojSIjONI7fNTEqYKnU2Tqqif4ue6qF4ImCJf7ExeI13zTupH3W/h9kfU8HNMug62PDq06SJ0qohJadon31g+3xXdN/9NjE8/nS58BF/wK/vF9khNSYe43xTnSpoq4jRFITUqlV+rF7rdjVplol0WwmstG3GdEDFngbkEWbbZ4GDLnjLyPvQaKVw6Ixk07hODc//uWK0WJ6RdPseLCX0d3W5K9hP0tn8K+V0BjJmCaSlJ3dfz38aG3Yfb14xKecww5bKjfMPwGPseYG7quO7yOK4qviOmuff959w91o+ozMCuSsLfvEU6n7qbhxfQzTApymRyDykB3sHvcDYb8PX6cAeeQOIvjjSlJyZv7Wimx6LAaBhauTZpE1JIFhbqVjY1t/HXVX8d97DxDHvWuAZf97s7dWDSWMQnPrZ5W6l31nJVx1rjPe6qQY8jhqVVPcdO6myg0FUYfv/Hs+CWsU1Om8mHDhxzqOkSxqRiNUkN1VzWSJMV8viNSBJv/2JpYTXaESVJCEj2RHgI9AdQJamwBG2XaY7j/n+EMpxDlqeU8sOSBk30ZE+es7w382zIN9q+Nv13lm7D4pyfmmr4kPLb7MQK9AWq6arhj4R0n+3LOMAmka9N5cMkYI/5OQ2akzuC5g8/Ffa7B3UCO4eRUmk1N11PV5qaqzY0v2MvqWcc2TpmfPp+t128lHAmTqBimh85xZFTp/sEHH+SZZ57hsssui4rOILKfv+p0uAJ0eoL0RsbW8G4wba4Bx/NYMKlMGHRhFEo/qwpWMd08nQR5AkaVEaVciUFlQKdSkW5Q0+jwj/t6RuIXZ/+CP13wJ4wqY0xHcldwEhzPADe9TihlKv5wLwZ1rKCYaUyixSlej1ltGRL3EY8WbxNW1VTyzFqSkxRjKoW16tWoEuTcc9l0PqmZnOZTrkAYw1Eu5mKLjsOdnpjHNld3cl7J5JV35hjFscIhLRZtSkypZ5uvLdpQcHXR6slvUKQ2kKLUiwaDvWEh7KQUiU7QhUvg8ydg5a9EVu8n/wMphXhCHkpS01AlyMnpczyna9PJ0mWRa8hFLpNTZCxife16ylLKUMqVp1djJbkCVDrRLO1U58N74f/Oh87hc65GxF4j/t4gohxSCiF9JlhKR94vvXwgGiMeZZdB0CUmB4NJSBRRGi/eKBY4hnNV5ywcWdgejTnfAE0qutatMPNq8Zg+A9zDN14FschVbCqmtrsWv7Mej1xOiu4YRLT+THxvX2zGaE763rBwN+csFPvsfg5e/Aacd/vANhozHFwHe56P2TU7HOa+T1+Fjx+AN24jkFImGvL4jlpsdbeJz/n8W2Lztkeh0Fg4stg2Dsdzm7ctRsQC4t8jDFmYZUrs9kOiEkNnOSM8nwCSVcl0BcYft9HgaiBLnxWzoHAiMGmUuAM93LqiJOZ9ZExSEg6kICmcGBINE8r7y9Bm0BXoItATAGBXxy4uL7qcI64j0XLKh754KPp8PwfsB/jL/r+wPHf5hBoEnUroE/X8svSX3HPOPaNuu6pgFfpEPcHeIJm6TFLUKagSVLT7YseCXYEu9Er9SZlIDYdMJsOkMvHr7b/m5UMvY/fbzziez3CGUxHr9PjRaZIkxjWjLfCfIYY2Xxvt3nbKzGWYk47dyHWGMxxvipOLafG24A3HmgMlSWL94fXMSpt1Uq6rNF3PgRYXD75bxb9eOGXMuc4jIZPJTtpYaVjb4LXXXjtk4tbvMHjhhYl3KP8y0eYK0BuRsHtF47bx0O4KcP6UsYuNJpWJC2foeLvezzzrPK6ecnX0uWx9drQTZ380RX956GTQLy7PscxhZ8f/b+++w9sq7/aB3xq29vbeI3HixAnZQBYzjBACZYQSVoFS4G3eFii0lJZfSwm0L7SUlkLfQilvSYEQVlugLauQFJJACNmx48SJHcfblm1Zspat8/vj8ZItD9my5XF/rouLWDo6eo4tPba+53vu5ytM67hUPCJRGx2aWn0wa/sWFFPMalQ1eyBJEjZ92oyCaRUD7keSJDj9zZhjsCHLpsUBzdA+sFp0sfjigfOhipFj4zuHQxaNw9XcI9+5U268HsdquwvPFU1u1LV4UZAauZXOc20JQBPQ6tEg2RB8qefumt1Ynro8Ys/Vh8oIa4xOFJ6dNSKPV9ExzeSeKy7Jzz0XWP24uPR95hq0vHERjCoDpifqu6I2ZDIZ/nXlv7p2O90yHW8dewvX50/QTC6NVUSLDLGDM2qctYBtOvDu90RkRbjsx0WRs9PF/zP87umeEmYCiXOAlBDZ3klzxIrji0bxKhyZDLjsd6javgWpnQveGZKDFx3sR445B8eajsGKCqRIipFdqmVIFoXn1npRlB1oMRxAZDkbk0X0iLNWLLB4zgNiocdOWitQ8rEoTLf5RDEfEJ3kyacBV/8f8Pul8FjyAEeheF/re/zuaioXJxgSZonH+FrFQpGDSNQmwul3osXX0h3p0VNrw5Dfc4+ypwAAIABJREFULy2+FhhjhvC7KKkAtuptaHBVAcnzROHe/sWQnoOGz6w2B50AHaoXDr2As9LOGoURDSzTqsOqWYk4u9ffaGZtDCoafVBJyTg3Y3i5/Qq5Aqn6VJQ5ynDUeRQ7Knfg4WUP442jb6DOXQdfuw8vHHwBF2VdhFk2caJNkiTc+t6tWJC4AHctuGvExzceqBXqIUV9GWONePysx7vWFAHQlfvc8/F17rqI5jNHikllwgdlH6DKVcWoDaLxypor/p7zOkWjSKfmU0CMWuRB05BIkoRqVzVeu/S17vV+iMa5GHkMplum45WiV7BY3h3n+c7xd3Cy5WTUur1nJBnx0N8PYU6aCRfMGtur/0ZDv4XnJ554YizHMSHVOLzi/83hF56H0/HcJrnQ4nPAFBtcyEnTp3V9qEu3alHe2BrWWIZqfsJ8fFXzVdeK7Y2expFHbXRobPX3yXcGAIM6Bgq5DHUtXtTY1dA3Ddxh6G5zA5Aj2WhAfrIRe4xDLx6btGLbeRlmfHHcjvNH+AYXxevgt1hOvA4n7a1oaw9AqZDjo8IanDMzISJnsDpl2yyQHYtBm1+LJH13x7PT58SOyh34yZk/idhz9aEywqpQw+62A/IqUSjrNHON+KMuVgfMuarr5hZ/C4yxRmw4Z1q/BfjO3Nl86wS9TFVjEYXncBz5p4iPWLcpdHzEaGhtEIXJV28QHdrhFo0bSkQHcqfU8DNQ+3X7NkAeomi78l7RVa4Y5fgVUxocWRcjtfNrQ5JY8HAQ08zTUNJUgpR2JVIVI8yUM6aKrnJXPZB++uAdz3VFQPxMcQLIVQdUtwGLeuUxam1A6X/Ev53V3VEprfWAziaKzN/6GM3lDUhu2NGxwGBB9+M7O5OVsUDcNKC2EEhbOOihyGQyZJuycaL5BObGz+27gdsenNU9gH6L170t/Q5sf3oZdRqT+HCpT2TG8xgYTsfzpxWfYn/dfryxtp/Ln0dRhk2L527su3CLSRODcrsbqViNK6ZfPuz9Zxoz8ds9v8Wh2kNYPW01ZsfN7novdMZwlDnKugrPTd4myGQyPH3e08N+zolOo+y+YiXPkofixmKsTFvZddtIFhYcTZflXoY58XNw54d3wt/uh1EZmWYNIooghVIsGF1XBKT1mPtrD/e90o4G5PA5oJQph3VFEFE0/XDJD/HE7iew278bv5/9ewDAi4dfxP1L7o9ah3B+sgHetgA2Xh7+YtbjUb+tV9u3b0dqaio2b96MV199Neg/EmocHsQq5ahxeAbfuJfqZi+STGEUntVmNHmaRJexKvgP1zRDGqxq0RmWbulejC/SFiQuwOfVn6M90A4AKHWUIsuUFZF9212+PvnOnVJMGuw43oCA3wS7d+AiQaO3EbEyAxKNKpyXn4h7V4T/QWReuhmHKoewaNcgHO62Ph3P6hgFEowqnOz4GX1YWIvz8yN7BivdqkV7mxaWWCMs6u5V1T8u/xgLExdG7GRBSForrFCIjueWyu6F5gDRMbB0Q9DmASkAl98FXYwOFxUkI80SulNyumU6TCpTVFZgjYjhFJ5rC4HCt8XifGOltQEwZYjc9ZKPw3tsoF102FqyR2VoIYvOgIjosGSNznMOpLPjeZCOjhxTDkqaSnCquRRp6hFmqveM2kg/XWQ8O2vF9z6U2o7Cc2ekRP1RIKHXyRutDWj3iaK2o0chvdXeHXVhSoOkiBX76Yz56NquRyRGYkf3+RB1FttCCiNqY8iFZ7UR8fO/gWprR3FdH496ZxVa/aPzO5OE4XQ8f1L+CdbPXB9UcIw2szYG5fZWZGmWIc0wtJMioWQZs7C9Yjvuz7sf31/8fcTIY7reC59VfIZUfSpKm0u7tq90ViJNP/znm2ymW6b3WWCwtLl0zBehHIpvFHwDCxMXYqZ1JowqY0QXLySiCAoVt1FzSNxOQ9a5lhDRRFMQV4Anz3kSe5r2oLa1FnWtdahwVmBh4uDNNKNldooJW79/NnLiI5dkEE39Fp6TkkSBJycnB9nZ2UH/TSX+9kC/l4rUODyYnWJEdZiFZ397AM1uH+L0qsE37mBWmVHhrIBaqe6T73d68uldi81kWLURz3julGPKQZI2CZ+c+gQAcLz5OLJNkXk9NLb6YNGG7k5OMavx2bF6zIhLg1dqQlt7W7/7afI0QS7pw+5A7ynVrEVV88i/h/3FdZyebcMHh2vg9Lbhq7LGsCJXhiLVrIHUrkWCxgSTytQVtfFh2Ye4MOvCiD5XH/EzEe9pFfmLjl4dzyG0+luhVqgH/TC2IHEBHln2yMQ92zecwrOrTqy6/e+NgD/8k1vD4uqIN5h+AXB0gIXfQmkuFycXhhCzMCnEakVczCA/186O5wpXFdK0A78fBmVMESd0OgvPNYeA3y4QGc2h1BWKQnOsDpArxeN7r8yutYmO6JT5wZnVrnpA2+vyUn1iR8dzDz0LxAn5otg9RNnGgQrPjZEvPANYPPcmfO6rhz/gx5eeWlwW24RXil4Z/IE0bBaVRZyMDENpcylyTDmDbziGTJpYnLS3jjiG6+z0s/HjM36MTG13R1ieJQ9vHXsLu6p34ZoZ16DUUdp13ynnqYgvfDeRJeuS+2Q8H28+Pu5eLz2dnnw6s06JxrOEWaLDuaeaQ+x4DlOVqwrJuhH+rUsUJcZYI5bZlmHLkS3YXrkdZySfEfUTxsmm8dOAMVL9Fp5XrFgBALj00kuRlZWFtLQ0pKamIiZmYi9qEo5AQMLXnvkM6/6wo8+CcABQ3ezBaWlm1IZZeK5t8cKmU4UVr2BSmVDqKO0TswEAS1OW4qo8EV2QbtV0ddOOhvX56/FKofiQfrzpOHJNAywIFga7ywdrPx3PyWYNPjvWgOW5SZAFtNhTWd7vfhq9jZDatEgwDL2o3/f51KhoikDh2e2HUdN3srru9Ay89PlJvPDpCZyRY4NeFdkJTadSIsY7Bzn6TJhVolMeAIobi1EQVzDIo0coIR/JzgZUu6pFEcswcIfyUAtGKoUKZ6WPfdZnxGgsfRdlG4yrDshaDiQWdBeBG0qAon9EfnyAWIjO7wLUZmD6KuDoB4N28wZpKBFZv1PJYDnPJ7YhzuNCu9SOA54apBpH2JHX1fHcIIq8KQvEZaH9LQbZ2fEMiMiMpBDvf61NLABpTOnV8VzfN9cw1GJ8PbOYE/JFsXuIBu94HjzjWZKksArPibpEZBmz8EHpB7h71yNY5XLjcMPhwR9Iw2ZWhd/xfKL5RMSuqIoUszYG1Q5PyN/r4ViQuABX5l0ZdNtVeVdhbe5anJd5HhYkLuiK3ACACmcFUvWpvXczZdk0NjS4gxeBPtF8ImKNEKPh/IzzsTR5abSHQUT9CdXxXHuYHc9hqnZVT9yrU4kAXJJ0CbYc2YKXCl8a3XWxpqBB/3resGED/H4/amtr0d7ejoSEBKxZs2YsxhZ17x8WHRUFqSb86dMTeORrc7ruc/va4WkLIC/RgH3l4X2gqm52IzGMmA1AdAxVuaowwzJjwO3SLaOX8QwAF2RegMd3PY4j9iOoc9eN6HLTnppafSEzngHRwVvR5EZ2vA56exz+UViExemhP2A0eZvg82mQbB7+2aFUswaVkSg8e9pCdkbNSzfDoFbihe2leOe/R2dCy1ZcgdkWFSwqoMHTAH+7HzWtNaN/uW7CLCQ3lqNSKwGBQHfRq5dNhzeh0dMICRLMqimw2I7WGn7Hs7NWdKLOuRo4sAWYtRb46s/Al/8H3LVPFLMjqdUu9imXiwKyUiXyhOOmh96+rhgo/hew7Dvi64YSEXsxlRiSxAmWxBAdMY5K4NUbIJPJkTttNvZ6y5BmC/1+GDJdgijIyuQif/vmd4G9rwDHPuy7bXubiOKIy+t+bGKIwnPOWUB8nvj59ex4DhV1oU8EqntFabQ2ANaO+TghX0TEDFGWKQsnHCEKz21esajPEDKe3W1uKOXKsPLXzsk4Bz/d8VNcPu1yfP3Uc9hQH2I1ewD+dj/erX4X+fkTNFt+nLCoLTjUEPp7HIrL74LD5xh3H15NHdFZI+14DiVGHoPr8q8DADR7m1HmKOta0LuipQK55ik2tw4gThOHBk/fwnOOefye+JxhnYEZ1hkoLBz6/EhEY6iz8CxJwImtwGsdizDHD/y5m4Kx8EwTXbI6Gb8865f43tbvYWkKTxhHUr8dz52cTieef/55zJ07F2+++Sa8Xu9YjCvqJEnCbz86iu+el4eV0+P7dBHXODxINKqQZFKh2uGBx98+5NVbq5u9SDKG15FrVpkRkAJ98p37bKeNgSQBza3+sPY/VDGKGKzKXIXnDjyHdEN6xC4/sLv8/Xc8dxTpc+L0mJWQjrf2H8LRmpaQ29Y67fB41ciwDv9y/2STGlXNnhGvxis6nvt+QJXJZHho7Ww8d+NCpIygQD6QJ6+Zh8VpWuSac3G86TjKneVI1CYiRjHKVyzYpiGlqRLVzipIjop+ozY+OvkRiuxFsHvs+PmKn4/umMaDYUVt1IsO01mXAce3iseXfiY6U3c8I7Yp/wJo6v8KgLC09opWyFwKlH3W//al/wE+eBA42lH0rC8GbP0UqScrQ0pwx3Pxe8Dhv4l/f/QzsZDf5b9HbnMtJBmQmjAn9H6GSqEUBWRtXPeCk3HTgYajfbe1Hxfvv87ok8ylQNaKvtulLxGvsd4dz676voVnXXyIjuceWdDGVMDvFh3ZQ5CkS0Jta8f+HFXisQBwapcohg9hcctwup07rcpcBV2MDnecdgcy1TbUe+rR4uv7O6XMUYYPa0MU9SksPdcaGIpSRykyjBmQywb9E3VMmTsLzyF+r0eSSWWCUq7EV7Vf4XDDYVS42PHckzHWCHebG9528Xmk0dOItkAbbGpGWRDRMOk7conrjgB/2wB87Q/A948DMZPnMvexwKgNmgyWJC/B1mu28iRKhA36V71SKQqLbrcbarUafv/oFDTHm0OVDji9bTg/PwEZtr4L9tU4PEgyqpFoVKOyyY01T32K9w7V9LO3YJ2PDUfngnChojZ6kslkSLeOctdz1gV4v/T9iF7WeKLeiTRL6F/uncXZnHgdss0pWDYzBr/5KEShBUCJvQZWtSWsGJPeDOoYKOQyNLtH9loXGc+hC/OLsqxYmDn4ZeTDlRWng1Iug0VtgSZGg52VO8dmhWFlLHTmTMTIFGhyVgUvLthDo6cRdy+8Gw8tfQjTLVOgWDmswnNHx7PGDEw7H/jyBdFNevX/AbueEwvKffD/ugudI9W7wzVzKVC2vf/tW6qA7JXA298VYzm5UxQxpxJDkvg+dNrzF+A/T4hFFovfA1bcA0w7D9NcTTC2B2C0Thv5cxpTgiMwbNNEt3LvE2V1hcHZhKseAjLPHOBYkoOPpbWhb9SGMVWcYPA6g7frPGEhk4UVt2GIMaAt0CYW9/vgQfG9A8TCljlnD2kfwyk8Zxoz8cFVH8CqtkKhT8IMXSqK7H2zqStdlYhXRTaDfyqyqCxo9Ax9/ittLkWWMWv0BjRMJu3odTz3lmXMwm3v34YfffojVLSw8NyTTCaDVW2F3S3iq443H0e2OXvirgFBRNEnkwFZy4D/XQ7MWA3kXdB9gp+GjB3PNFmMt+aHyWDQ7+iqVavwu9/9DjNnzsS6deug10+OVRUH8/a+Slx6WjJkMhnSLBpUNnvQ1h7our/a4UFCR+H5aK0TJ+pd2FU6tAzXBpc3rIUFAUCj1EClUHUVoAeSHafF5yfCzJMNw4KEBbCoLRFbyCUQkLC7rLHfQmyqWQO9SokEgwpWjRVGnbffKIxTjnokG0be9SLiNka2oJvD3dZ1aW40zbDMwAdlHyBjpPmyQ5WQj2SlDpWtdf12PDd6GmFRRzgqYjwLt/AcaBfbdxb0Fn4D2PoYkHwakDBTfF/LtgOnvgQcFcMbU+9Cpase0PUsPC8PLjw7qoDjnwR/PedqQG0UUQ+NpWKBuqmkZ8azJAHln4t4kg9/Cpz2dUBlABQxyEtZgqwAgEhccWBMFickOmnMoiOnZ9EY6IhJCaPQ3Zkf3alnQblT/Awg91zg7//d/frpfcIifuaQ4zZkMpm4bN7dIDqn9/xFRIQc/wTIHlqmu9PvhD42/L9Luq7W0cUjP9aGP+z7A9a8tQa+dl/XNhXOChaeI8CsDi/judRROu7ynQHArBFXZY0043ko1s1Yh9+d+zsEpABKHaVcXLAXm9rWFbdxovnEuF5YkIgmiGv+Avy/emD1Y9EeyYRV01qDJC0Lz0TU16CF5+uuuw4bNmzAt771LTz88MP43//937EYV1QFAhLe3leJtaeJDhOVUoE4XSyqmrsLkVXNHiQb1bBqY2FQKXHPqrwhZz3bXT5Y9UPPowTEB3STyjSkwvPd5+fh6Y+P4cUdpfj7vsoRR0b0ppArcEvBLTgzJbh7ztcWQCAgnsvb1j7k/R2tdcKqi0V8PwsCplu1ePc7yyGTyWBT29CGFtS2BEe+lDvK8V7pe6h1NiDTPPJCQbJJHVTcrnd6w+6AdnhCR22MtZnWmdhds3tsOp4BILEAyZ5WVCkUovDWS3ugHQ6fY2pkO3fShJnx3GoHVEYRrQCIiARjsuhCBkTx75OfA4E2kYU7HO/cBfzj+/0XEG25YsHB3y0GfjkDeOYM4OVrRP4u0LF4ZIroDPngJ2KRu9GOchlvjMnd8RRNHYuBnXYtcOgtYMFNXZstmn8bfq+M0PvPmNq3E9k2HajvdRWIo0psO1SdHc9/+zZwanfojGeZDLjkCaD0U3GiAei7XZg5z3GaONS56wBPE+BtAbb+AqgrAtJPH9LjHT5H2B3PQUypmCtTo9RRCo1Sg4/LP+66q9JZifhYFp5HqrPjeah/i7DjGbg091IsTV2Ka2deC6vaCm3M8OPDJqOuE1YASppKxvXCgkREU8Ujyx+J2PpPRDS5DFh4/stf/oL169dj9erVuOWWW3DgwAGo1eFFRExEFU1uxBlUmJHU/WE23Roct7HnZCPmpJkgl8uw44HzcOOZmThc5YC/R1d0f+qdPtj6yTMeiFllHjRqAwCmJxrwq6tPw+cn7Pj9JyX4wRv7I158vmn2TViYuDDotvvf3I+XvziJY7VOXPjrbUPe165S+6CxE5k2HQCRFekONKOuxRt0TNsrt+PpvU+j0duE6XEjP9OaYtagsrm78PzDNw9g8xcnB33c58cbcLjSAaAj43kMPqAOJs+aBwnS2BWeT78dKYEAqvTmkJepOXwO6GP1EcsHnxA0FsDd4yqENi9w5F/9b++qFfnOneRy4GvPAou/Kb7OOUfkL2evHF7Hs98NHHxLdJZ+3nEysdUe3OEqkwG3vg+sexH41sfAvUdFrEPtYXG/o0oUXmeuFtEKWcvCH8dEZ0rvLjif/FwUSxfcCMy7TnSmd5DlnAXjje9E5jnjZ/bN0o6b1jfn2VHZb9RNSCq9yFQu/wLY8yIQowWUIX5PxajFsdmPi858r0N0XXeNpZ/M6f4ORxOPOncdngvYsWP+leI1efnvxfMMQYuvBcaYgdc+GFBcHlZ7Anj3indx0+yb8Naxt7ruqnBWIEGVMMCDaSg0Sg1i5DFD7no+1nRsXC6mN1YZzz1dNu0y/PiMH4/Z800UNo0N9e56AEBxY/GgC28TEdHoW5i4kLFHRBRSv4Xnp556CsXFxXj00UexZcsW/OAHP8CuXbvwzDPPjOX4oiLdqsVf/yu4iJJh1aKso/AsSRK+LG3E4ixRLNWrlDCoY5Bq1uBIdehF73qyu3yw6sKL2gBE19BQOp4B4JyZCXh6/QK8fseZ+M/RepxyjH42956TTfh3US0+OVKLk/ZWtAeGVuzeXdaIxVlDi12wqCxw+JoQq5DD4W7rur3KVYUTzSfgkioxOylCheeOqI16pxcfF9XC6W0b8DEtHj++/fIebNpZ1vF125hckjuYzg9kmYYxKjyrDEiefzMqp58LAPC0iYUa99TuwT2f3CNiNlRTKGYD6Bu1Ufg28Nc7+t/eVRccpwAA6YtFoRcQnc+KWNFd2zyMwvPR94GUecD6zcDW/wGcdR2LC/bqcLVkig5WY4ooQiafBlTtE/e1VIou2eT5gDFtyNEIk0pcnshXDrQD5TuBjDOA5LnA5SF+T8ojlBW26GbgrPuCb0uYBVTsEd3rO38v4io6O9LDcddBYPXjQNG7fV8LPVlzROHZ3SQ68+WK7vs6M6eHqLOAtFXuww5zPPDND4FZa4f8+OFkPAeJy4O84ShUChXOzzgfB+sP4s+H/oxWf6voeGbUxojJZDKkGdJQ3jL4Qqjedi9OtZzCNHME8tAjTBurQIxCBkM/azeMBo1Sg1WZq8bs+SaKzqgNSZJQZC/CDCsLz0RERETjVb+fhD/99FP87Gc/Q1ZWFvR6PWbMmIGf//zn2L59gMWmJhF5r8XpMm1anOwoPB+vd0Edo+ha9K7TvHQz9g4hbkMUnsPveF6WuizsP651KiVmJhlQ0Ty6hecWjx9VzW58ccKO9w/XICABDU7v4A+E6HheNMTCs1Vjhd1jR7xRhdqWHtEnriooZApA0YJptsRhHUNPKebuqI2/7RW5py7vwPEhv/v3MSSZVF2vgWb3+IjayDBk4PyM85GsH7tVhpPN2aiSie7/7378Xfzqy1/hyd1PYmflTtg99qmV7wyITlKPAwh0XBGx9yVRiPY4Qm/vDFF47ilWC1y7GSi4QkQdtPn63zaUg28Ac64SBcQ564D//DJ0tEJvHYVnWZtbdG1rLKKgeudnU29hQUD8HAyJgP1ER8fzGdEZx+yvAUVvi4Um/3U/YC8Jv+MZECcX0haL12bvOI+erDnimEOdrDCli9eSzzWkp4zXxKO+tQ4nFDIUtwx+VUlvkSg8o/4oIElQK9V4btVz2HZqG/544I8sPEdQuiF9SIXnY43HkGHMQKwi/L+RRptMJsMfb1o8rCvWKLJsGhsa3A2odlVDpVAhTjPAfEVEREREUdVv4Tk2tu8f1nK5HAqFIsTWk1+6VYttxXX45p93YcuX5SE7dAtSTThc1U8hqYcGpxdxYWY8A8DNBTdjlm1W2I/LidcP2PFc6/Dgni17u/KZh+NQpQOzko2YnqjHV2WNyLJpUe3of3E+SZLgbWtHdbMHLm8bcuOHtjiUVSUKzwkGVVDOc7WrGjm6xQAQkaJmklHTNf5/HKjChbOT4Bqk4/mjolpsvHwOTtQ70eLxo9XXBn1s9DueFXIFfn3Or8c02iLNkIYyRxnaAm3YW7sX75W9B7vHjgACONlycup1PCuUIsrAVScymSu+EsW7zpiG3lx1wVEboUw7D1CqAH2i6G4Nx8mdIicaAFbeBxx8U9ymG1rhOcZdBxiSuqNUNFMor7u3+HyxqGBjqeh2jgZDksgB/9u3gRgdUHNIRKcM9hoKJVYHJM/ru7BgT9YcoPFE6JMVcgVgyRYd0UMQr43HEXshWuUyFDcdG3DbgCRO3Oyp3YPXil8DEIHCs9Yq3p/OWgBAvi0f31v0Pfyt5G9wt7lhUg7tKiMa2FALz0X2Isy0zhx0u2g5Ky+elxGPA3GaODR4GtjtTERERDQB9Ft47u8P60hnBU8Us1PEh8/ceD2e3XYci7P7ZhJn2rQ42dDa5/aefG0BtPraxzT7NztON2Dh+YPCGrz5VQX+cbAq7H03unz45p93YXdZIwpSTVg5PR4LMizIjdejxtFdGJYkKWixvle+KMcdm3bjyzI7FmZahvxBzqgywu13I06v7NPxHHAsRKxcA5Ui/BiT3hKNKtR2FJ7LGloxL90Ml2/gwnOtw4NMqxYzEg146t/HMCfV1KdzfqqYYZmB2tZabK/cjmRdMp5d9SweW/kYUvWpOFh/cOp1PAMi9/f/LgH+b42IS4ibATT2V3iuHbjjtCdT2tDjNgLtgN8jIhI6Yxj08SIawlExeMdzYgFQW4gYV1X4MQ6TVcJMYM8mIGV+dBdXXPxN8ZpZcCNQ+h9RdJYP80Rx5tKhRW301yVvywUaehWRJSnk6zROE4evavdidpsET7sHdo+9zzadrvz7lShuLMbrxa9j2ymxjsCIFxcExHux/kjXl/nWfGiUGiTrk1lkjJBwCs/M66XB2NQioqeocXyfqCAiIiIioN8WyK+++grLly/vc3tzc/OoDmi8mpagx7vfWQEAOHtGAgpS+y5mlGnTocwe+vLi5lY/TNoYNLb6YNbGjmlBMideh80DRG18cqQOX5ufiic/PIqLC5KhCGNspQ0ufFhYi+0lDfjp2tk4Pz8Rl8xNxguflaLG4cHO4w04WuvEZ0fr8VFRDd64cynmppnx3qFq/OdoHbQqJRZlDbywYE9ymRwmlQkmjbersN0WaEOduw7+0nT86fbnhryvgSQY1ahxeOFvD6DZ7UOmTYvtJfX9bu/xt8PjD8CsjcFp6Wb88T/H8eTX50dkLBORUq7EGcln4Om9T2NewryuFedT9ak4UH8Ay1P7zi2T3kU/FwU9lRHIOQv45w9El2wozlqxUN1QmFKHtsDgrj+KTuvl94is6J6Zw9NXAbd+ACTOGXgfKj1gyYax7L3uvOmpLj4f+PTX4vsaTbnnAN/eBex/VYzHMIKfz+l3AN4B1iuwZInXblN56C552zSgvlfhefcL4jX/zQ9F53yHOE0cXG2tyEEMlJY8HLEfwZkpZ/bZZUAKoLS5FG8Uv4Gtp7Yi2yjmlBZfC4yxI1hcEBALItYXi8U6IU68X5x9MQ7UHRjZfqlLuiEdfy/5+6DbFdmLcEHWBWMwIprIbBob6lrrsL9uPy6bdlm0h0NEREREA+i34/ngwYP49NNP+/x34AA/iJ2Za4MhRMdyqlmDmmZRrHR4ugu9HxfV4vxfb4W/PYAGp2/M8wFz4vqP2vC1BbCzpAE/viQf7QEJxTWDL47YU22LF/nJRrQHJMxNM8Gqi0VeogGJRhVqHB48/fExvH+oGllxOjx+1WnY8PIe1Do8+LLUjvPyE/H1vOrmAAAgAElEQVTu/iosygyv+9WitkCn8aK2o/Bc766HVmHCvPQ4LEk5bZBHD41epYRcBhyvc8GmU8GoiYHL13/Gc12LF/EGFWQyGeZnmJFkVOPigpEvcjiRrUhbgcMNh3FafPfPJFWfiqONR6de1Ean/EtF0RkAzJn9R23UHxXFu6Ewpor4jk4VXwV/DYhO589+A1TuAZpPihze3tKXDG0BvLPvh7n0HyMrbE4mCR3ddhlRynfuSRkrMovtJeHnO/dkSu0+rlBidSLf+9MngDlX973fNi2447mpHPjoYWDF94DXviHywTt0ZrNmy7XIs+ShuLE45FM2ehohk8mwpXgLJElCo1cs1un0OyPQ8dyR89zD9TmX4+7cK0a2X+qSbkjHqZZT/d5/rPEYrv/H9TjccBh5lrwxHBlNRAnaBNS769HgbsCixEXRHg4RERERDWAIVYbI8Hg8+O///m+sX78et912G+z2vpfTbtmyBVdccQXWrVuHjz/+eMDHvf/++zj//PNxww034IYbbsAXX3wxVofSr1ilHAlGFUrqnFj683+jullENfx9XyUaXT78u6gWdpcPtmHkO49EolEFT1sAze6+xecvy+zIidfBpldhVrIRR6rDLzzPSzdj+/3nYmZSd9dZklGN6mYPDlc68NhVc3H/xTNx+fxUnJUXj2ue3Yk5aSbceGYmYhVyFKSGl6FpU9ugUrd2RW1UuaqgkdkwO2WEXW+9JBrV2HeqCYkmNXSxygEznmtbPIg3iIiPS+em4LU7lyJGMWZvr3Gps6v5tITuwnOaIQ3tUvvUjNrozZIVOmpDkoDaQiAhf2j7MaUFF5r/vRH46sXgbYreEYVC+3HRqWrOGO6ogfxL4Uw6Y+iF8ckuLg+I1YtF+caDuOni/yMpPA+FNUe8Rju6hPuMoaFHIffAFqDgSuDs+8XXTd2LCFrVVsggQ3aMCdPM01DSVBLy6ercdcgyZWFu3FxcmnspGj2i8DzijGego0M7uPBs+uKPmPHXuyFrD3PhTgopQZuAZl8zPG2h135498S7yDHl4MWLX4RJxVxtGpgh1oCd63diy6VbuLAgERER0Tg3ZpWxV155BXl5eXj55Zdx+eWX45lnngm6v66uDps2bcLmzZvx/PPP44knnoDP5+v3cYcOHcJ9992HTZs2YdOmTViyZMlYHcqAMm1avLWnAk5vGz4/0QCPvx0fFdbgngvy8OqucjS4vLCOccezTCZDqjEGJ+r7xoAcONWMhZki6mJmkgGF1f0vjtjWHsChyuColTqHBwkGFWz64FzlRKMaByqaIUEUoTv96JJ8aGMVuGBWEpblxuGv314GdUx4OaQWtQUKpatrccEqZxXQbkF2nC6s/QwmwajCvvImJBlV0KkUaB2g47nW4UWiUXwPlAo5Us2aiI5lIorTxOHZVc92XRIPiI5nIDILQE54lszQURvN5SLSQjvECJqUBUDZZ+LfkgRUfgWU9zoRd/hvwOLbAF0CUPrpyArPMhnKV/xKZAkTEKMB7ikc+s9rtGmtYmHA0S48n347cPFjoe+zZAP2E91fVx8E0jq6ErU2kQ3dQSlXIjHGgFx1HOI18WjwNCCU2tZaJGgS8Kuzf4XvLvguWv2t8Af8cPgc0McObXHaftlygxdDbPOK3G6tBaYTb49s3wRALHKbrEvut+v545Mf46q8qzA7bvYYj4wmKuavExEREU0MY1Z43r17N1asEBnJK1euxI4dO4Lu379/P+bPn4/Y2FgYDAZkZGSgqKio38cdOnQIb7zxBtavX49f/OIXaGsbeOG3sZJh1eGN3acQp4/FzuMN+ORIHWalGPGNpVnYXdaIw1WOMY/aAIB0UwxKap19bi9vbEWGVRRJZyQZ+nQ8twe6F5N8ZVc5rv/j50H313ZETPSWYFShqLoFs1OMQR8O1DEKvPlfS/GNpVmQy2WYNYwuZavaCknuRF2LFw/teAhvHXsLXrcROfEjLD70kmBQY/+pZiQZ1dCpBu54rnF4kGBQ93v/VHVmyplBP38WnnswZ4rOz94LtobT7QwAqQtFHm9toShkSwERtxEIdG9TdwRIKhCdqCUfh47aCIdcMfyF6yYjdWSvthixuLzRX/xx1mX9v071CUCbpzsnuvoAkNSRH66xAq3BVzy9nnk10nVJsGqssLtDLy5Y11qHBG0C4jRx0Cg1MKqMaPY2i9s1CSM7FnOmyElv77gqqPBtIGEWcOlvYD7x7sj2TV0K4grwZc2XfW6vdFfC4XOgIK4gCqMiIiIiIqLRFHbh+dlnn8Wzzz47YKH3tddew5o1a4L+a2lpgcEgLofV6XRoaQkucDqdzq77O7dxOp1Bt/d83LJly/Dggw/ipZdeQmtrKzZv3hzuoYyKTJsW9U4fbl+Zi53H7fjfrSW4dkkGtLFKrJ6TjJd3noRV17dQO9qyLLE4EiK/udzuRrpVCwCYmRQctfFRYQ3m/PQ9XPHMZ3h110n89qOjcHja0Orr/tnXtniREKLwnNjR5TwruW9BRqVUjGhxRYvagjZZC6qbW/FOyTuod9fD4bBGvOM50ahCUbUDSSYNtLGKQaI2Qn8fKFhn4dmqGifdodGk0ov/OmMymiuAN74JVO8XRa+hkstFEfDQX0W3c9YK0fVaVyTuD7QDDSWiGBk/A3DbAfMIC880vq1+DMi7MHrPL5OJrvrGMsDnEq/xuI7c3l4dzwBg8nsAtQlWlbUru7m32tZaxGvju742q8woc5RBIVOMvONZGSsyyzujb3b9EVh8K5C6EKXn/mFk+6YuF2RegPdK3wu67e2St/H08adxbsa5kMumdjwVEREREdFkpAz3Afn5+Zg9ezYcDges1tDFo6uvvhpXXx284NCGDRvgcomoB5fLBaMxuCCp1+u77u/cxmAwBN3e83FXXnll17/PO+88vPde8IeZ3rxeLyRJQmFhYRhHGz6F2wmlHFhoduM3Djekdj+mxTajsNCB0+Pb8Iq3Db4W+6iPo7dUnQz/PFaF3k9bUt0Ef6MKhYV2BCQJTS4vvth7EC5fAHe9W4GHz0uCw9OO13cewxmpKuypDGDb7kPINIuu7ZO1TWhtUKKwMLhYEJAkKOWAWXJG/Fh9TT6ccpUCynho5Frcn/Ewbv7yJOrKS1Df69JLj8cz/Od3N8PfLiHgbEBZiRdufzsOHT4MeYjLO4+W12J2onrMf64DGdGxj6K1yWtRX1aPJnnTqOx/LI47UvNJcvxieLY+j8a8axC/72nYjryJ9hgdak/7DprD2LdGNw8pn/8MrsTF8GvToDK2oXXX39GUK0OM8xQyY004VnISZr8ByQCO1fvg9wx/7OP1tTXaJs5xK4HGCgAVEdtjuMeeprSh6cCnaNMUIsmQgdJisdhggltCW1kR7JrufSVUnkCbNgG1J2tR31of8nmKK4uRpc3quk8VUOGTw5/AorRE5GeSrk5C475P4NcWIr3uGI5JOUBhITz+9gkzn4x31oAVhfWFeOuLt+ANeDFdNx2P7HsEN6fejCX6JZP++HubOPNJ5E3VYx+r4w4EAlPy+wvwtTUV8dhH99g5n0y9Y5+qxw3w2Efz2AcsPDc1NcFsNgMAGhoaIJPJumIvwrVgwQJs3boVc+fOxbZt27Bw4cKg++fOnYsnn3wSXq8XPp8PJSUlyMvLC/k4SZKwdu1abN68GUlJSdixYwdmzx44F1ClEt2o+flhXMI+DMZkNyp8x7Fg7mxcX6HAiulxmD1NLHwyU5Lwh6+cmJuXifz8pFEdR2+1zgN4dq8r6PgDAQl1raVYuWg2tLHipTAzpRGSMRn2RjdWzmjDlWfNBwDc3NE8d8Pzn0NlSUb+THFpc8tbFVgydyZSQmQaZ8fVYfUZs5Bpi2wncr2hHvsO7UOctQVphhzE2FIxPbEJs2b17RItLCwc9s98jq8S+NKO+fk5KMiNg0p5Elm5edCp+r5tvDtacFpeVtf3ZTwYybGPpkfyHxnV/Yd73MOZYCM2n8R8E/j4ESRd/H3g7X8AVz4H5eu3IGXeeUhJDWPf0kyg8TPEfvVn4Ia3gIYSmKr3Izk/HzhSBiTPFmPVNAC7H8O0+StFl+cwjdfX1mibqscNDOPYTxTAoG8HYlqAzMXdj62fDngcSOy5ryI5kDkDCbMXILAngKzpWdAog3+n+Cv9mJMzB/kZ4nGp1aloUDYgy5YVmZ/J8bnQ6/xAwyfAkluRP1tEg0yo+WQCONd+Ln5+9OeIVcRiTc4aLE9fjpUJK6fEsffG+WTqHftYzCcAIJfLp+T3F+BrayrisY/unML5ZOod+1Q9boDHPprzSb/XNX7xxRf42te+huZmsZhcUVERrrzySnz5Zd98vqG49tprcfToUVx77bV49dVXsWHDBgDACy+8gI8++gjx8fG44YYbsH79etx00024++67oVKpQj5OJpNh48aN2LBhA66//nq43W6sW7duWOOKtFSzBj9dK4rg9188E8umda+2LZPJ8NI3T8e5UShOxusU8PjbUe/0dt1W5/TCoFZ2FZ0BYG6qCV+WNWJveRPmp5v77CfNosWpJjcAkf9sd/kQpw8dMfHP766IeNEZAObGz8XB+oPQ6upgjknF8TpXxGM2AHRFZ3QujqhTKeDyhY7bqHV4QmZdEw0o+yygqRzYvB5IPwMouBK48W9A8mnh7UcmA9b8Grjof8R+4qZ3L+5Wf0REbABA4mxg1toRFZ2JhsSSKaIrqvaLfPFOGmufqA14mgG1CTKZDFa1FY2e7itoJElCW6BNLC6o7f7daVFbcLjhMJJ1yZEZrzUHOPUlcOB1YOFNkdkn9XHf4vvw7tfexe1zb8dLhS/h6ryrB38QERERERFNWP12PD/55JPYtGkTTCYTAJGp/Kc//Qk/+tGP8PLLL4f9RBqNBr/97W/73H7zzTd3/XvdunV9Csj9PW758uVYvnx52OOINmsUFhYERNE7vyPDOW6aKJCW21uRZtEGbXdefiKe/LAY/nYJl8/vuzhVmkWDU42tAAC7yweDOgaxytDnL5SK0clrNKlMSNIloSHwFTRYhRP1TmTHRXZhQaA7pzrJ1Fl4VsLlbQcMfbeta/EiwcjCM4VJoQTO+aEovC2+TdyWc/bw9iVXAGfcIf5tzgCaOvJq64qBtEXi3xoLsO7FkYyYaGjMmcDxT4CaQ8Dpt3ffrrUB7l45zp4mQC1OdHYWnlP04vfPa8Wv4YOyD0TGsyY44/mE4wTW5K6JzHhtucB7PwTm3wAYR3lhximsc2HZa2deC7VSjSVJS1DUVBTlURERERER0WjptzKoUCiQlpYWdFt2djbkci7+MlHNTDagsMrR9XV5YysyrMGF59NzrDha68TR2hbMTjH12UeaRYOKRtHxXNviidqCevMS5qE5UAJ5WwKOVDsxPTHyhedkkxoXFyR1dYRrY5UhFxi0u3xwePywRWHRSJoEFt0CLL9bLDQYKcY0oKUaaG8L7ngmGiuWLOD4VkBlABJ6XLaltQKt9uBtOzqeAVGYtHu673+v9D3sq9uHRm8jbBpb1+1WtRUBKRDBjudcADJg2V2R2R8NSClX4uq8qyELsWYCERERERFNHv1WkSVJQiAQCLqtvb0dfr9/1AdFo2NGkgHFNS1dX5fb3Ui3BudoqpQKrMyLx/QEA9Qxij77SDVrcKqr8OyNWrzEvPh5AACfOw6FVQ7MTjEO8ojwqWMU+P313VnkepWiT+G5xePHjX/6HN9amQOFnB+gaZxQxgK6eMBRAdQWAfEzoz0immosmUC7F5h1efDtoaI23I2ARnQ8W9QWNHpFR3SztxmHGw7joaUPIVGbCKW8+yItc0eHdGdn9IjZcoE7PwPipkVmf0RERERERNR/4Xnt2rW45557UFRUBKfTiWPHjuG+++7DxRdfPJbjowjKsulw0t7a9XVxTQvSe0VtAMDXF6fjigWpIfeRatGgoiPjubLJ3ZV/PNbmJcyDSq7FiRoFmt3+kMcRadpYJVp97UG3/enTUmTH6XHvBewopXHGlA6U/kcU9LTWaI+GphqVQbwGC64Ivl1rA9w9Op4lCXDVATqR32xRWboynred2oYlSUtwcfbFeH3t60G7sarEazpiHc8ymchAJyIiIiIioojpN+N53bp10Ol0ePTRR1FbW4vU1FRceeWVWL169ViOjyIow6rFyQZReD5Y0Yydxxvws8sK+my3Yno8VkyP73M7ACQY1Ghu9cPjb8f+8mbMTesbxzEWsk3Z+PWyl3DDs0ewOMsC+Rh0G/deXNDjb8emnWV45bbTebkwjT/mDKDoXSCx73ucaExs+BKI6XVyUmMRHc6SJIq9rXYgVte1nU1jQ4NHdERvPbUVZ6efDQAwxgZf1WJWm6GQKRCniQMRERERERGNT/0WngHgkksuwSWXXDJWY6FRlmxSo97pg7etHT97+zDuvWBG2IsdKuQyZMVpUVjlwJ7yRtxwZuYojXZwBYkZAI5gVnLkYzZC0fXKeP77vkrMTjFiemKI1QaJos2cAez4HbD0O9EeCU1VvYvOgIiBidGKXGeNGWipAvRJXXdbVBaUOcogSRJ2Ve/C9xZ+L+Suk3XJWJS0KCh+g4iIiIiIiMaXfj+xXXPNNf12cW7evHnUBkSjR6mQI9msRmFVCw5WNuPl204f1n7Oz0/EG1+dwqlGN2YkRa/oatbGQKWUY9Yo5DuHolMp4fJ2R23sP9WEc2cmjMlzE4XNnAG0eYCkOdEeCVEwjUXEbWjMYhFMQ4/Cs1pEbRxrOgatUotkfegoDYvagj9e8MexGjERERERERENQ7+F5yeeeGIsx0FjJMOqxTv7KlGQYoJS0W/E94AuKkjCFc9sx4IMC2KGuY9IkMlkyLRpMSfVPCbPp+u1uGCjyx92xzjRmDFniP+z8EzjjdYmIjasOYAzuPBsVVvR6GnEF9VfYEnykigOkoiIiIiIiEaq38JzamroxeVoYsuwavHO/ipcetrwF2Sak2pColGN+ZljU/AdyF+/vQza2LG51Fobq0SLp7vwbHf5WHim8cuSBahMgDl6cThEIWmtovAMiKiNHoXnJF0STjSfwJtH38TNBTdHaYBEREREREQUCdFrV6WoyLBqUe3w4LT04ReNZTIZ7jp/Oi6ZM/zidaSMVdEZAHSxvTqeW32waFl4pnHKmg3813ZAzmmexpn4mcD7PwaOfyKiNvTBheffnPsbWFQWnJF8RvTGSERERERERCPGVXmmmEybFgAwbwSFZwC4elF6JIYzoehUSrh8wYVndjzTuGZKi/YIiPq6YCNgmwZsfVzkPGetCLp7cdJiLE5aHKXBERERERERUaSwFW6KyYrTId6gQqpZE+2hTDhicUFReJYkCY0uP8zamCiPiohogpHJgDlXAZV7gKYywBD9q2eIiIiIiIgo8lh4nmJmJBrw9oblkMlk0R7KhGPVxaLe6QMAuHztUCpkUMcoojwqIqIJSGUA4mcA1QcAQ2K0R0NERERERESjgIXnKUYmkyHJpI72MCakdKsWpxpbAQCNLuY7ExGNSOZS8f8eGc9EREREREQ0ebDwTDRESUY1Gl1+ePztsLuY70xENCJZywG1GYjhyVAiIiIiIqLJiIVnoiFSyGVINqtR0eSGvdUHCwvPRETDl7UCOOeBaI+CiIiIiIiIRgkLz0RhSLNoUG5vRVOrD1YuLEhENHwqPXD67dEeBREREREREY0SZbQHQDSRpFu0KG90w9cWgJkZz0RERERERERERCGx8EwUhnSrFqfsrYhRyJnxTERERERERERE1A9GbRCFIc2iQXljKzOeiYiIiIiIiIiIBsCOZ6IwpFu1KLe7IUmAlVEbREREREREREREIbHjmSgMGVYtjtc5sf9UMyw6Li5IREREREREREQUCjueicIQp1fh99cvxL+LajEzyRjt4RAREREREREREY1LLDwThWllXjxW5sVHexhERERERERERETjFqM2iIiIiIiIiIiIiCiiWHgmIiIiIiIiIiIioohi4ZmIiIiIiIiIiIiIIoqFZyIiIiIiIiIiIiKKKBaeiYiIiIiIiIiIiCiiWHgmIiIiIiIiIiIioohi4ZmIiIiIiIiIiIiIIoqFZyIiIiIiIiIiIiKKKBaeiYiIiIiIiIiIiCiiZJIkSdEexFjYu3cvVCpVtIdBROOM1+vFvHnzwnoM5xMiCoXzCRFFynDmE4BzChGFxr9RiChSwp1PpkzhmYiIiIiIiIiIiIjGBqM2iIiIiIiIiIiIiCiiWHgmIiIiIiIiIiIioohi4ZmIiIiIiIiIiIiIIoqFZyIiIiIiIiIiIiKKKBaeiYiIiIiIiIiIiCiilNEewFgIBAL46U9/iiNHjiA2NhYbN25EZmZmtIc1ai6//HIYDAYAQFpaGu644w7cf//9kMlkmD59On7yk59ALp9c5xz27duHX/7yl9i0aRPKyspCHu+WLVuwefNmKJVK3HnnnTjnnHOiPewR63nchw4dwh133IGsrCwAwLXXXovVq1dPuuP2+/144IEHUFFRAZ/PhzvvvBPTpk0bs5855xPOJ5N1PgGm3pzC+WTsTbU5hfMJ5xPOJ6OH8wnnE4DzCeeTyOB8wvkEmJzzCRD9OQXSFPDee+9JP/jBDyRJkqQ9e/ZId9xxR5RHNHo8Ho902WWXBd12++23Szt37pQkSZIefPBB6f3334/G0EbNs88+K61Zs0a6+uqrJUkKfby1tbXSmjVrJK/XKzkcjq5/T2S9j3vLli3S888/H7TNZDzu119/Xdq4caMkSZJkt9uls846a0x/5pxPOJ9MxveVJE3NOYXzydiaanMK5xPOJ5xPRg/nE84nPU22Y+d8MrY4n3A+6WkyHnu055TJc8pmALt378aKFSsAAPPmzcPBgwejPKLRU1RUBLfbjVtuuQU33ngj9u7di0OHDmHJkiUAgJUrV2L79u1RHmVkZWRk4Kmnnur6OtTx7t+/H/Pnz0dsbCwMBgMyMjJQVFQUrSFHRO/jPnjwID755BNcd911eOCBB+B0OiflcV900UX47ne/2/W1QqEY05855xPOJ5PxfQVMzTmF88nYmmpzCucTziecT0YP5xPOJ5xPOJ9ECucTzieTeT4Boj+nTInCs9PphF6v7/paoVCgra0tiiMaPWq1Grfeeiuef/55PPTQQ7j33nshSRJkMhkAQKfToaWlJcqjjKwLL7wQSmV3akyo43U6nV2XznTe7nQ6x3yskdT7uOfOnYvvf//7eOmll5Ceno6nn356Uh63TqeDXq+H0+nEd77zHdx1111j+jPnfML5ZDK+r4CpOadwPhlbU21O4XzC+YTzyejhfML5hPMJ55NI4XzC+WQyzydA9OeUKVF41uv1cLlcXV8HAoGgF9tkkp2djbVr10ImkyE7OxtmsxkNDQ1d97tcLhiNxiiOcPT1zF7qPN7erwGXyxX0hpoMVq1ahYKCgq5/Hz58eNIed1VVFW688UZcdtlluPTSS8f0Z875hPPJZH1f9TZV5hTOJ2Nnqs8pnE84n3A+iRzOJ5xPOv/N+YTzyUhxPuF80vnvyTqfANGdU6ZE4XnBggXYtm0bAGDv3r3Iy8uL8ohGz+uvv45f/OIXAICamho4nU4sW7YMn3/+OQBg27ZtWLRoUTSHOOpmzZrV53jnzp2L3bt3w+v1oqWlBSUlJZPudXDrrbdi//79AIAdO3Zg9uzZk/K46+vrccstt+C+++7DVVddBWBsf+acTzifTMb3VShTYU7hfDK2pvqcwvmE8wnnk8jhfML5BOB8wvkkMjifcD4BJu98AkR/TpFJkiSNeC/jXOeqrMXFxZAkCY8++ihyc3OjPaxR4fP58MMf/hCVlZWQyWS49957YbFY8OCDD8Lv9yMnJwcbN26EQqGI9lAj6tSpU7jnnnuwZcsWnDhxIuTxbtmyBa+++iokScLtt9+OCy+8MNrDHrGex33o0CE8/PDDiImJQVxcHB5++GHo9fpJd9wbN27EP//5T+Tk5HTd9qMf/QgbN24ck5855xPOJ5N1PgGm3pzC+WRsTcU5hfMJ5xPOJ6OD8wnnE84nnE8ihfMJ55PJPJ8A0Z9TpkThmYiIiIiIiIiIiIjGzpSI2iAiIiIiIiIiIiKiscPCMxERERERERERERFFFAvPRERERERERERERBRRLDwTERERERERERERUUSx8ExEREREREREREREEcXCM1EIN9xwA0pKSqI9DCKaBDifEFGkcD4hokjhfEJEkcL5hAbCwjMRERERERERERERRZQy2gMgGim/34+f/OQnKCsrQyAQwF133YWHHnoIixYtwtGjR2EymfDEE08gJiYGDzzwAMrLy9He3o6bb74Zq1evxr59+/DII49AkiQkJibil7/8JQDg6aefRn19PdxuN5544gmkp6dH+UiJaLRxPiGiSOF8QkSRwvmEiCKF8wmNNRaeacJ77bXXYLFY8Oijj6KxsRHXX389PB4PLr30UixevBiPPfYYXn31VcTExMBiseDxxx+H0+nEFVdcgTPOOAMPPvggfv3rXyM3NxcvvfRS1yUiZ511Fi677DI89dRT+Ne//oXbbrstykdKRKON8wkRRQrnEyKKFM4nRBQpnE9orLHwTBNecXExdu/ejf379wMA2traoFQqsXjxYgDAggULsG3bNigUCixduhQAoNfrkZubi/LycjQ0NCA3NxcAcN1113Xtt6CgAAAQFxeH+vr6sTwkIooSzidEFCmcT4goUjifEFGkcD6hscaMZ5rwcnJycMkll2DTpk147rnncNFFF8Hn86GoqAgAsHv3bkybNg25ubn48ssvAQBOpxPFxcVIS0tDQkICSktLAQDPPvssPvjgg2gdChFFGecTIooUzidEFCmcT4goUjif0FhjxzNNeF//+tfx4x//GNdffz2cTifWr18PuVyO5557DpWVlUhJScHdd98NAHjwwQdx7bXXwuv1YsOGDUzH+jEAAADaSURBVLDZbHjooYfwwAMPQC6XIz4+Ht/4xjfw4osvRvmoiCgaOJ8QUaRwPiGiSOF8QkSRwvmExppMkiQp2oMgirRzzz0X//znP6FSqaI9FCKa4DifEFGkcD4hokjhfEJEkcL5hEYTozaIiIiIiIiIiIiIKKLY8UxEREREREREREREEcWOZyIiIiIiIiIiIiKKKBaeiYiIiIiIiIiIiCiiWHgmIiIiIiIiIiIioohi4ZmIiIiIiIiIiIiIIoqFZyIiIiIiIiIiIiKKKBaeiYiIiIiIiIiIiCii/j+qnk82RKdIxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x576 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=5, nrows=2, figsize=(20, 8), sharey='row', sharex=True)\n",
    "\n",
    "for n in range(2, 7):\n",
    "    df = avg[avg.n_factors==n].pivot(index='epoch', columns='units', values='ic_mean')\n",
    "    df.rolling(10).mean().loc[:200].plot(ax=axes[0][n-2], lw=1, title=f'{n} Factors')\n",
    "    axes[0][n-2].axhline(0, ls='--', c='k', lw=1)\n",
    "    axes[0][n-2].get_legend().remove()\n",
    "    axes[0][n-2].set_ylabel('IC (10-epoch rolling mean)')\n",
    "\n",
    "    df = avg[avg.n_factors==n].pivot(index='epoch', columns='units', values='ic_daily_median')\n",
    "    df.rolling(10).mean().loc[:200].plot(ax=axes[1][n-2], lw=1)\n",
    "    axes[1][n-2].axhline(0, ls='--', c='k', lw=1)\n",
    "    axes[1][n-2].get_legend().remove()\n",
    "    axes[1][n-2].set_ylabel('IC, Daily Median (10-epoch rolling mean)')\n",
    "\n",
    "handles, labels = axes[0][0].get_legend_handles_labels()\n",
    "fig.legend(handles, labels, loc='center right', title='# Units')\n",
    "fig.suptitle('Cross-Validation Performance (2015-2019)', fontsize=16)\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(top=.9)\n",
    "fig.savefig(results_path / 'cv_performance', dpi=300);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll average over a range of epochs that appears to deliver good predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T01:42:55.807996Z",
     "start_time": "2020-06-23T01:42:55.798227Z"
    }
   },
   "outputs": [],
   "source": [
    "n_factors = 5\n",
    "units = 8\n",
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T13:31:21.275251Z",
     "start_time": "2020-06-23T13:29:04.968477Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "predictions = []\n",
    "for i, epoch in enumerate(range(130, 170)):\n",
    "    epoch_preds = []\n",
    "    print(f'\\nEpoch: {epoch}')\n",
    "    for fold, (train_idx, val_idx) in enumerate(cv.split(data)):\n",
    "        X1_train, X2_train, y_train, X1_val, X2_val, y_val = get_train_valid_data(data,\n",
    "                                                                                  train_idx,\n",
    "                                                                                  val_idx)\n",
    "\n",
    "        model = make_model(n_factors=n_factors, hidden_units=units)\n",
    "        model.fit([X1_train, X2_train], y_train,\n",
    "                  batch_size=batch_size,\n",
    "                  epochs=epoch,\n",
    "                  verbose=0,\n",
    "                  shuffle=True)\n",
    "        epoch_preds.append(pd.Series(model.predict([X1_val, X2_val]).reshape(-1),\n",
    "                                     index=y_val.stack().index).to_frame(epoch))\n",
    "\n",
    "    predictions.append(pd.concat(epoch_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2020-06-22T23:45:09.013Z"
    }
   },
   "outputs": [],
   "source": [
    "predictions_combined = pd.concat(predictions, axis=1).sort_index()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2020-06-22T23:45:09.326Z"
    }
   },
   "outputs": [],
   "source": [
    "predictions_combined.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2020-06-22T23:45:10.093Z"
    }
   },
   "outputs": [],
   "source": [
    "predictions_combined.to_hdf(results_path / 'predictions.h5', 'predictions')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:ml4t-dl]",
   "language": "python",
   "name": "conda-env-ml4t-dl-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
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  "toc": {
   "base_numbering": 1,
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
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